Reuben R. McDaniel, EdD

Changed

Sun, 05/21/2017 - 13:25

Display Headline

Nighttime clinical encounters: How residents perceive and respond to calls at night

Author(s)

Luci K. Leykum, MD, MBA, MS

Jacqueline A. Pugh, MD

For hospitalized patients, restrictions on resident duty hours and the hospitalist movement have led to fragmentation in care.[1] After 2003 duty‐hour regulations were implemented, one study estimated an increase of 11% in care transfers for a given patient, whereas another study reported that an individual intern participated in 40% more handoffs.[2, 3] Although these changes have represented an improvement in safety with reduced provider fatigue and increased expertise in inpatient care, tradeoffs in safety may occur. Communication breakdown during care transfers has been implicated in many medical errors,[4, 5, 6] and the ability to safely transfer a patient's care has been identified as a necessary clinical skill.[7] The Accreditation Council on Graduate Medical Education has mandated that training programs include education to ensure effective handoff processes.[8] The Joint Commission has developed a toolset for improving handoffs.[9] Taking cues from the military and other industries that operate continuously, approaches designed to standardize handoffs have been developed.[3, 10, 11, 12]

The use of handoff tools has been reported to reduce the time required to transfer care from one provider to another,[13] but evidence that these handoff tools improve quality of care is limited.[14, 15] Concern that patients have poorer outcomes in care transitions remains, particularly at night when many patients are cared for by covering or night float providers.[6] Studies regarding the outcomes of patients at night have had mixed results.[16, 17, 18] Uncertainty is inherent in the trajectories of individual patients and in the systems in which they receive care.[19] The recognition of uncertainty reframes care transitions from a problem of improving information transfer to a problem of navigating uncertainty, or making sense. Sensemaking is an activity through which providers come to understand what is happening with a patient, in a way that allows them to take action.[20]

We sought to better understand how to support providers' ability to make sense and act in uncertain situations, focusing on night float resident physicians. We hoped to better understand overnight encounters and the information needed to navigate them. We approached the issue in two ways: first, through assessing resident attitudes and perceptions of handoffs using survey methodology, and second, through assessing actual calls night float residents receive and strategies they use to navigate these scenarios. We focused on handoffs between the primary team and covering nighttime providers. Our goal was to use this information to understand what approaches could better support care transitions and handoff practices.

METHODS

General Approach

We surveyed residents regarding handoffs. We also collected self‐reported information about calls received by night float postgraduate year (PGY) 1 (intern) residents and the strategies they used to address these calls.

Setting

Our study was conducted in the internal medicine residency program at the University of Texas Health Science Center at San Antonio, which has approximately 90 residents, 76 of whom are categorical. Residents work at 2 primary teaching hospitals: the Audie L. Murphy Veterans Affairs Hospital (ALMVAH), the 220‐bed acute care hospital for the South Texas Veterans Health Care System, and University Hospital (UH), the 614‐bed county hospital for Bexar County.

The residency program implemented a night float system in 1992. Daytime care is performed by multiple teams, which are comprised of one attending, one resident, and two to three interns. These teams sign out to the on‐call team in the late afternoon to early evening. The on‐call team in turn signs out to a night intern who is supervised by a resident and on‐site faculty member. The night float intern is responsible for all patient care on five inpatient teams until 7 _am the following day, but is not responsible for admitting patients. In the morning, the night intern discusses overnight events with the day teams as they arrive.

Sign‐out consists of verbal and written communication. At ALMVAH, written documentation is created within the electronic medical record. Basic information is prepopulated, and clinical information is modifiable. At UH, written documentation is created in word processing software and maintained within a document saved electronically. It is expected that the day team update the modifiable information within these documents on a daily basis. The written documentation is printed and given to the covering interns (see Supporting Information, Appendix 1, in the online version of this article showing the sign‐out tools used by our program.).

The day team is responsible for the content and level of detail in the written sign‐out. There are three domains including: main diagnosis, clinical history and course, and plans of care. The clinical history and course is a synopsis of the patient presentation including current clinical status. The plans of care are reserved for expectant management or conditional statements.

Survey Development

A survey regarding resident experiences and perceptions of handoffs was developed by the Department of Surgery, and we adapted it to the internal medicine residency program. The survey contained 48 questions focused on the following areas: attitudes toward night float, communication content, and night float behaviors (see Supporting Information, Appendix 2, in the online version of this article for the full survey). Some responses were recorded in a 5‐point Likert‐type format, in a range of strongly disagree to strongly agree. Others were recorded on a 4‐point frequency scale from never to always. Paper and online survey versions were created, and residents could respond using either modality.

Survey Administration

All residents were asked to participate in the survey. Paper versions were distributed in March 2012. All residents also received an e‐mail soliciting participation. Responses were collected anonymously. Reminders were sent on a biweekly basis for six weeks. Survey administration was concluded by May 2012, and no incentive was offered for completion.

Overnight Call Data

We asked the night interns at both hospitals to self‐report activities in real time during their shift. To minimize respondent burden and obtain a representative sample, they collected data on their activities over 2‐hour periods. On any given night, a predetermined period was assigned, and all periods were sampled equally over the duration of data collection. A total of six interns at both hospitals were asked to participate over 18 nights during a 3‐month period in 2011. Convenience sampling was used, and participants were identified based on clinical schedules.

The tool allowed interns to record unique encounters initiated as a phone call or page. Open‐ended responses were permitted for caller identification and encounter reason. The interns categorized the source of background information and were permitted to select more than one for any given encounter. Similarly, the interns were asked to categorize the type of action required to respond (see Supporting Information, Appendix 3, in the online version of this article for the self‐report tool).

Overnight encounters were categorized as clinical, administrative, or pain related. Clinical encounters consisted of calls related to clinical conditions that would require clinical assessment and decision making, for example, a patient with new fever. Administrative encounters consisted of contact for reasons that would require only acknowledgement from a physician. An example of an administrative encounter is restraint renewal. Pain‐related calls consisted of calls for patients experiencing pain or requests for new or additional pain medications.

Analysis

Frequency and percentages were calculated for each category of encounter, including callers and reasons for calls. Comparisons were made between reasons for the encounter, the sources of background information utilized, and actions taken in response. Survey data were analyzed using Microsoft Excel (Microsoft Corp., Redmond, WA).

RESULTS

Encounter Data

Data from 299 encounters were recorded, and 96.7% (289/299) encounters were complete. Clinical encounters were most frequent at 54.7% (158/289), whereas administrative notifications or pain‐related encounters were 32.9% (158/289) and 12.5% (36/289), respectively. Nurses initiated 94.8% (274/289) of encounters.

Sources of information used by interns varied by reason for the call and are shown in Table 1. Responding to clinical requests, interns most frequently interacted with a nurse alone or in combination with the chart (51.3%, 81/158). Responding to administrative notifications, the interns most frequently spoke to only the nurse as the primary source of information (44.2%, 42/95). In pain‐related notifications, the nurse alone as a source of information accounted for 33.3% (12/36) of encounters. The sign‐out tool was not used in 72.3% (209/289) of encounters.

Table 1.Sources of Information by Encounter Type
Information Source	Encounter Type
Information Source	Clinical^*	Administrative	Pain Related	All
NOTE: Combinations of information sources not shown had no encounters. *Encounters related to conditions that required clinical decision making. Encounters related to notifications that only required acknowledgement by the physician. Encounters related to pain management. All types of encounters. ^‖Chart source indicates electronic medical record. Miscellaneous sources include laboratory, radiology, and others.
Only tool	2 (1.3%)	6 (6.3%)	2 (5.6%)	10 (3.5%)
Only nurse	30 (19.0%)	42 (44.2%)	12 (33.3%)	84 (29.1%)
Only chart^‖	28 (17.7%)	14 (14.7%)	5 (13.9%)	47 (16.3%)
Only miscellaneous	4 (2.5%)	2 (2.1%)	0 (0.0%)	6 (2.1%)
Tool+nurse	10 (6.3%)	8 (8.4%)	5 (13.9%)	23 (8.0%)
Tool+chart^‖	10 (6.3%)	5 (5.3%)	1 (2.8%)	16 (5.5%)
Nurse+chart^‖	51 (32.3%)	12 (12.6%)	5 (13.9%)	68 (23.5%)
Nurse+miscellaneous	1 (0.6%)	0 (0.0%)	0 (0.0%)	1 (0.3%)
Chart+miscellaneous	3 (1.9%)	0 (0.0%)	0 (0.0%)	3 (1.0%)
Tool, nurse,+chart^‖	19 (12.0%)	6 (6.3%)	6 (16.7%)	31 (10.7%)

Use of miscellaneous information sources was infrequent; removing these left 279 encounters with complete information. To better assess the instances in which the handoff tool was used, we combined categories for information sources. These data are summarized in Table 2.

Table 2.Consolidated Sources of Information by Encounter Type
Information Source	Request Type
Information Source	Clinicala	Administrativeb	Pain Relatedc
NOTE: Miscellaneous source of information from Table 1 removed. a Encounters related to conditions that required clinical decision making. b Encounters related to notifications that only required acknowledgement by the physician. c Encounters related to pain management.
Only tool	2 (1.3%)	6 (6.5%)	2 (5.6%)
Only nurse	30 (20.0%)	42 (45.2%)	12 (33.3%)
Only chart	28 (18.7%)	14 (15.1%)	5 (13.9%)
Any combination with tool	39 (26.0%)	19 (20.4%)	12 (33.3%)
Any combination without tool	51 (34.0%)	12 (12.9%)	5 (13.9%)

The actions taken by interns varied by reason for the call. Clinical encounters had the most variety of actions taken, with 55.1% (87/158) resulting in a new medication order and 49.9% (78/158) handled over the phone. Bedside evaluations occurred in 23.4% (37/158) of the encounters, and 3.8% (6/158) were documented in the electronic medical record. Administrative encounter responses were more homogeneous; 96.8% (92/95) were handled entirely over the phone. Responses to pain‐related requests were similarly less varied than clinical encounters; 63.9% (23/36) were handled over the phone and 66.7% (24/36) resulted in a new medication order. Neither administrative nor pain notifications resulted in documentation in the electronic medical records. These data are summarized in Table 3. Despite the availability of a resident and attending overnight, only 6.3% (10/150) of the clinical requests led to a discussion with them; none of the administrative or pain‐related notifications involved discussion with either the resident or the attending.

Table 3.Actions Taken by Physician by Encounter Type
Actions Taken	Encounter Type
Actions Taken	Clinicala	Administrativeb	Pain Relatedc
NOTE: More than 1 action may be taken for any given encounter. a Encounters related to conditions that required clinical decision making. b Encounters related to notifications that only required acknowledgement by the physician. c Encounters related to pain management.
Handled over the phone	78 (49.4%)	92 (96.8%)	23 (63.9%)
Evaluated the patient at the bedside	37 (23.4%)	2 (2.1%)	2 (5.6%)
Reviewed previously ordered labs or imaging	43 (27.2%)	12 (12.6%)	0 (0.0%)
Ordered new lab or imaging	44 (27.8%)	2 (2.1%)	0 (0.0%)
Ordered new medication	87 (55.1%)	1 (1.1%)	24 (66.7%)
Wrote cross‐cover note	6 (3.8%)	0 (0.0%)	0 (0.0%)
Conferred with supervising physician	10 (6.3%)	0 (0.0%)	0 (0.0%)
Called consult	3 (1.9%)	0 (0.0%)	0 (0.0%)
Upgraded level of care	1 (0.6%)	0 (0.0%)	0 (0.0%)

Survey Data

Fifty‐three residents completed surveys, for an overall response rate of 59.6% (53/89). All PGYs were represented; PGY‐3s had a response rate of 68.0% (17/25), PGY‐2s had a 58.3% response rate (14/24), and PGY‐1s had a 55% response rate (22/40).

A night float intern was perceived to be safer than an on‐call team performing the same job by 73.6% (39/89) of respondents. The written sign‐out was considered a time saver by 66% (35/53) of respondents. The sign‐out procedure was thought to be frequently or always safe by 73.6% (39/89). Overnight documentation within the electronic medical record was reported to be frequently or always completed by 58.5% (31/53).

Furthermore, 20.7% (11/53) of respondents reported receiving a do not do list frequently or always, and 43.4% (23/53) of respondents reported giving a do not do list frequently or always. Conditional statements were reported as frequently or always given by 90.4% (47/52). A standardized verbal checkout was considered safer by 71.7% (38/53), standardized written documentation was considered beneficial by 94.3% (50/53), and a checklist to go over was considered beneficial by 84.9% (45/53).

DISCUSSION

Our goal was to understand how to better support care transitions and handoff processes. Our residents report that current approaches to care transitions are safe and useful. Although this perception is reassuring, it is difficult to know whether this reflects the actual delivery of safe care. A minority of residents report giving and receiving do not do lists, which are important aspects of care when giving guidance to a covering physician. Also, we find discrepancies between our survey results and nighttime call collection data in important areas. Although residents report that the written sign‐out is useful, it was deemed useful for resolving a clinical issue only 27% of the time. Previous reports have found variable and conflicting rates of written sign‐out utilization, as well as variable quality of a written sign‐out,[21, 22, 23] and our data support infrequent usage. Residents were much more likely to access the electronic medical record than they were to use the handoff tool. Additionally, although residents report documentation, very little actual documentation occurred. The high rates of calls for routine and pain‐related notifications are notable and should be examined further for areas of potential improvement. Preemptive orders for routine, common, and benign conditions are often not employed as strategy and their omission can lead to higher workloads for nighttime physicians. Additionally, education and training may be necessary to help housestaff understand how such a strategy is safely implemented, such as a specific regimen for mild pain, and why it is helpful beyond reducing nighttime workload, such as a proactive approach to clinical care.

Several important insights emerge from our results. First, the electronic health record is accessible, and providers use it frequently. This raises the question of the need for a handoff tool for information transfer. When data can be easily accessed, their presence in a physical tool may be less important. Because electronic health records can easily be leveraged to populate handoff tools, having a brief tool that minimizes information transfer but better supports clinical reasoning may be more effective.

Second, our data highlight the need to focus on the handback, or providing information back to the returning day team. Our experience and previous studies support that this process is not adequately developed.[24, 25] There is little opportunity for communication between the covering and primary providers, and there is little documentation. In our observations, 3.8% of calls resulted in documentation, whereas the majority of respondents to the survey state it is performed frequently or always. The reason for this discrepancy is unclear, but fostering more of a mentality that considers all of the providers involved in patient care to be part of the same team may help address this issue.

Third, clinical services assume providers have what they need to provide care in the form of the handoff instrument. In fact, providers have handoff instruments, but whether they need them is unclear. Based on these observations, overnight physicians are able to provide care in the vast majority of cases without the use of the handoff tool.

Fourth, our data demonstrate the social or relational nature of providing clinical coverage. The single most frequent action taken by covering residents was speaking to the nurse. This may not be surprising; however, when we reframe transitions of care and handoffs as a relational issue, we are forced to reframe potential strategies to improve these transitions. The problem we need to address is not only of information transfer; it is also of making sense of what is happening.

How do we make handoff tools more effective sensemaking tools? More focus on contingency statements might be an approach. These have the dual benefit of helping the covering provider to make sense using the primary team's reasoning, as well as improving the primary team's reasoning by making the potential complications more explicit. Another approach could be to reinforce relational actions, through providing guidance on who to call if there is a change in the status of the patient. We found that the night intern rarely discussed care with supervising physicians, indicating weak integration of the night team. The handoff tool could thus strengthen the network of providers caring for the patient. A tool that emphasizes sensemaking may be a tool that captures the nonroutine aspects of care that are not already documented in the health record.

Our data are limited in that they were collected in a single institution over few nights with few interns. Our processes may not be representative, and our expectations for provider communication may not be the norm. Although a night float system of coverage is not the only model of providing care, it is common, and our handoff tool is similar to those reported in the literature. One area of concern is that our handback expectations may be less robust than other institutions. Despite this limitation, the larger issues of information transfer and sensemaking are generally applicable. Although we collected data over only 18 nights, we did obtain information on almost 300 calls, giving us a robust sample of actual issues that residents were called to resolve. Interns are the most involved in actually providing night coverage. Their response rate was 55%, slightly below our overall response rate of 59.6%, but representing the majority of interns. A 2‐step process of sign‐out may have ramifications on care transitions; however, these data were collected at night. Because the handoff tool information is the day team's responsibility, the process may have less impact on these results.

Coverage and care transfers are part of the inpatient landscape, and it may be unreasonable to expect care to be delivered by a group of providers who know the patient with the same level of depth at all hours of the day. By understanding that fostering effective care for patients requires providers to pay attention to not only how they transfer information, but also how they collectively make sense of what is happening, we will enable safer care.

Disclosures: The research reported here was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CDA 07‐022). Investigator salary support was provided through this funding, and through the South Texas Veterans Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. Parts of these data were presented at the 2013 SGIM National Meeting in Denver, Colorado. The authors report no conflicts of interest.

Files

References

Epstein K, Juarez E, Epstein A, Loya K, Singer A. The impact of fragmentation of hospitalist care on length of stay. J Hosp Med. 2010;5(6):335–358.
Horwitz LI, Krumholz HM, Green ML, Huot SJ. Transfers of patient care between house staff on internal medicine wards: a national survey. Arch Intern Med. 2006;166(11):1173–1177.
Vidyarthi AR, Arora V, Schnipper JL, Wall SD, Wachter RM. Managing discontinuity in academic medical centers: strategies for a safe and effective resident sign‐out. J Hosp Med. 2006;1(4):257–266.
Wilson RM, Runciman WB, Gibberd RW, Harrison BT, Newby L, Hamilton JD. The Quality in Australian Health Care Study. Med J Aust. 1995;163(9):458–471.
“Improving America's Hospitals.” The Joint Commission's Annual Report on Quality and Safety. Available at: http://www.jointcommission.org/Improving_Americas_Hospitals_The_Joint_Commissions_Annual_Report_on_Quality_and_Safety_‐_2007. Published 2007. Accessed November 17, 2014.
Arora V, Johnson J, Lovinger D, Humphrey HJ, Meltzer DO. Communication failures in patient sign‐out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005;14(6):401–407.
Myers JS, Bellini LM. Resident handoffs: appreciating them as a critical competency. J Gen Intern Med. 2012;27(3):270–272.
Accreditation Council for Graduate Medical Education. Common program requirements. Available at: http://www.acgme.org/acgmeweb/Portals/0/PDFs/Common_Program_Requirements_07012011%5B2%5D.pdf. Published July 1, 2011. Accessed November 17, 2014.
Joint Commission Center for Transforming Healthcare. Hand‐off communications. Available at: http://www.centerfortransforminghealthcare.org/projects/detail.aspx?Project=1. Accessed November 17, 2014.
Wacogne I, Diwakar V. Handover and note‐keeping: the SBAR approach. Clin Risk. 2010;16(5):173–175.
Chu ES, Reid M, Schulz T, et al. A structured handoff program for interns. Acad Med. 2009;84(3):347–352.
Starmer AJ, Spector ND, Srivastava R, et al. I‐pass, a mnemonic to standardize verbal handoffs. Pediatrics. 2012;129(2):201–204.
Haig KM, Sutton S, Whittington J. SBAR: a shared mental model for improving communication between clinicians. Jt Comm J Qual Patient Saf. 2006;32(3):167–175.
Li P, Ali S, Tang C, Ghali WA, Stelfox HT. Review of computerized physician handoff tools for improving the quality of patient care. J Hosp Med. 2013;8(8):456–463.
Starmer AJ, Spector ND, Srivastava R, et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014;371(19):1803–1812.
Fletcher KE, Davis SQ, Underwood W, Mangrulkar RS, McMahon LF, Saint S. Systematic review: effects of resident work hours on patient safety. Ann Intern Med. 2004;141(11):851–857.
Maggs F, Mallet M. Mortality in out‐of‐hours emergency medical admissions—more than just a weekend effect. J R Coll Physicians Edinb. 2010;40(2):115–118.
Khanna R, Wachsberg K, Marouni A, Feinglass J, Williams MV, Wayne DB. The association between night or weekend admission and hospitalization‐relevant patient outcomes. J Hosp Med. 2011;6(1):10–14.
Han PKJ, Klein WMP, Arora NK. Varieties of uncertainty in health care: a conceptual taxonomy. Med Decis Making. 2011;31(6):828–838.
Weick KE. Sensemaking in Organizations. Thousand Oaks, CA: Sage Publications; 1995.
McSweeney ME, Landrigan CP, Jiang H, Starmer A, Lightdale JR. Answering questions on call: pediatric resident physicians' use of handoffs and other resources. J Hosp Med. 2013;8(6):328–333.
Fogerty RL, Schoenfeld A, Salim Al‐Damluji M, Horwitz LI. Effectiveness of written hospitalist sign‐outs in answering overnight inquiries. J Hosp Med. 2013;8(11):609–614.
Fogerty RL, Rizzo TM, Horwitz LI. Assessment of internal medicine trainee sign‐out quality and utilization habits. Intern Emerg Med. 2014;9(5):529–535.
Devlin MK, Kozij NK, Kiss A, Richardson L, Wong BM. Morning handover of on‐call issues: opportunities for improvement. JAMA Intern Med. 2014;174(9):1479–1485.
Nabors C, Patel D, Khera S, et al. Improving resident morning sign‐out by use of daily events reports [published online ahead of print February 11, 2014]. J Patient Saf. doi: 10.1097/PTS.0b013e31829e4f56

Article PDF

Issue

Journal of Hospital Medicine - 10(3)

Publications

Page Number

142-146

Read more about Nighttime Clinical Encounters

Sections

Author(s)

Luci K. Leykum, MD, MBA, MS

Jacqueline A. Pugh, MD

Author(s)

Luci K. Leykum, MD, MBA, MS

Jacqueline A. Pugh, MD

Files

Files

Article PDF

Article PDF

METHODS

General Approach

Setting

Survey Development

Survey Administration

Overnight Call Data

Analysis

RESULTS

Encounter Data

Table 1.Sources of Information by Encounter Type
Information Source	Encounter Type
Information Source	Clinical^*	Administrative	Pain Related	All
NOTE: Combinations of information sources not shown had no encounters. *Encounters related to conditions that required clinical decision making. Encounters related to notifications that only required acknowledgement by the physician. Encounters related to pain management. All types of encounters. ^‖Chart source indicates electronic medical record. Miscellaneous sources include laboratory, radiology, and others.
Only tool	2 (1.3%)	6 (6.3%)	2 (5.6%)	10 (3.5%)
Only nurse	30 (19.0%)	42 (44.2%)	12 (33.3%)	84 (29.1%)
Only chart^‖	28 (17.7%)	14 (14.7%)	5 (13.9%)	47 (16.3%)
Only miscellaneous	4 (2.5%)	2 (2.1%)	0 (0.0%)	6 (2.1%)
Tool+nurse	10 (6.3%)	8 (8.4%)	5 (13.9%)	23 (8.0%)
Tool+chart^‖	10 (6.3%)	5 (5.3%)	1 (2.8%)	16 (5.5%)
Nurse+chart^‖	51 (32.3%)	12 (12.6%)	5 (13.9%)	68 (23.5%)
Nurse+miscellaneous	1 (0.6%)	0 (0.0%)	0 (0.0%)	1 (0.3%)
Chart+miscellaneous	3 (1.9%)	0 (0.0%)	0 (0.0%)	3 (1.0%)
Tool, nurse,+chart^‖	19 (12.0%)	6 (6.3%)	6 (16.7%)	31 (10.7%)

Table 2.Consolidated Sources of Information by Encounter Type
Information Source	Request Type
Information Source	Clinicala	Administrativeb	Pain Relatedc
NOTE: Miscellaneous source of information from Table 1 removed. a Encounters related to conditions that required clinical decision making. b Encounters related to notifications that only required acknowledgement by the physician. c Encounters related to pain management.
Only tool	2 (1.3%)	6 (6.5%)	2 (5.6%)
Only nurse	30 (20.0%)	42 (45.2%)	12 (33.3%)
Only chart	28 (18.7%)	14 (15.1%)	5 (13.9%)
Any combination with tool	39 (26.0%)	19 (20.4%)	12 (33.3%)
Any combination without tool	51 (34.0%)	12 (12.9%)	5 (13.9%)

Table 3.Actions Taken by Physician by Encounter Type
Actions Taken	Encounter Type
Actions Taken	Clinicala	Administrativeb	Pain Relatedc
NOTE: More than 1 action may be taken for any given encounter. a Encounters related to conditions that required clinical decision making. b Encounters related to notifications that only required acknowledgement by the physician. c Encounters related to pain management.
Handled over the phone	78 (49.4%)	92 (96.8%)	23 (63.9%)
Evaluated the patient at the bedside	37 (23.4%)	2 (2.1%)	2 (5.6%)
Reviewed previously ordered labs or imaging	43 (27.2%)	12 (12.6%)	0 (0.0%)
Ordered new lab or imaging	44 (27.8%)	2 (2.1%)	0 (0.0%)
Ordered new medication	87 (55.1%)	1 (1.1%)	24 (66.7%)
Wrote cross‐cover note	6 (3.8%)	0 (0.0%)	0 (0.0%)
Conferred with supervising physician	10 (6.3%)	0 (0.0%)	0 (0.0%)
Called consult	3 (1.9%)	0 (0.0%)	0 (0.0%)
Upgraded level of care	1 (0.6%)	0 (0.0%)	0 (0.0%)

Survey Data

DISCUSSION

METHODS

General Approach

Setting

Survey Development

Survey Administration

Overnight Call Data

Analysis

RESULTS

Encounter Data

Table 1.Sources of Information by Encounter Type
Information Source	Encounter Type
Information Source	Clinical^*	Administrative	Pain Related	All
NOTE: Combinations of information sources not shown had no encounters. *Encounters related to conditions that required clinical decision making. Encounters related to notifications that only required acknowledgement by the physician. Encounters related to pain management. All types of encounters. ^‖Chart source indicates electronic medical record. Miscellaneous sources include laboratory, radiology, and others.
Only tool	2 (1.3%)	6 (6.3%)	2 (5.6%)	10 (3.5%)
Only nurse	30 (19.0%)	42 (44.2%)	12 (33.3%)	84 (29.1%)
Only chart^‖	28 (17.7%)	14 (14.7%)	5 (13.9%)	47 (16.3%)
Only miscellaneous	4 (2.5%)	2 (2.1%)	0 (0.0%)	6 (2.1%)
Tool+nurse	10 (6.3%)	8 (8.4%)	5 (13.9%)	23 (8.0%)
Tool+chart^‖	10 (6.3%)	5 (5.3%)	1 (2.8%)	16 (5.5%)
Nurse+chart^‖	51 (32.3%)	12 (12.6%)	5 (13.9%)	68 (23.5%)
Nurse+miscellaneous	1 (0.6%)	0 (0.0%)	0 (0.0%)	1 (0.3%)
Chart+miscellaneous	3 (1.9%)	0 (0.0%)	0 (0.0%)	3 (1.0%)
Tool, nurse,+chart^‖	19 (12.0%)	6 (6.3%)	6 (16.7%)	31 (10.7%)

Table 2.Consolidated Sources of Information by Encounter Type
Information Source	Request Type
Information Source	Clinicala	Administrativeb	Pain Relatedc
NOTE: Miscellaneous source of information from Table 1 removed. a Encounters related to conditions that required clinical decision making. b Encounters related to notifications that only required acknowledgement by the physician. c Encounters related to pain management.
Only tool	2 (1.3%)	6 (6.5%)	2 (5.6%)
Only nurse	30 (20.0%)	42 (45.2%)	12 (33.3%)
Only chart	28 (18.7%)	14 (15.1%)	5 (13.9%)
Any combination with tool	39 (26.0%)	19 (20.4%)	12 (33.3%)
Any combination without tool	51 (34.0%)	12 (12.9%)	5 (13.9%)

Table 3.Actions Taken by Physician by Encounter Type
Actions Taken	Encounter Type
Actions Taken	Clinicala	Administrativeb	Pain Relatedc
NOTE: More than 1 action may be taken for any given encounter. a Encounters related to conditions that required clinical decision making. b Encounters related to notifications that only required acknowledgement by the physician. c Encounters related to pain management.
Handled over the phone	78 (49.4%)	92 (96.8%)	23 (63.9%)
Evaluated the patient at the bedside	37 (23.4%)	2 (2.1%)	2 (5.6%)
Reviewed previously ordered labs or imaging	43 (27.2%)	12 (12.6%)	0 (0.0%)
Ordered new lab or imaging	44 (27.8%)	2 (2.1%)	0 (0.0%)
Ordered new medication	87 (55.1%)	1 (1.1%)	24 (66.7%)
Wrote cross‐cover note	6 (3.8%)	0 (0.0%)	0 (0.0%)
Conferred with supervising physician	10 (6.3%)	0 (0.0%)	0 (0.0%)
Called consult	3 (1.9%)	0 (0.0%)	0 (0.0%)
Upgraded level of care	1 (0.6%)	0 (0.0%)	0 (0.0%)

Survey Data

DISCUSSION

References

Epstein K, Juarez E, Epstein A, Loya K, Singer A. The impact of fragmentation of hospitalist care on length of stay. J Hosp Med. 2010;5(6):335–358.
Horwitz LI, Krumholz HM, Green ML, Huot SJ. Transfers of patient care between house staff on internal medicine wards: a national survey. Arch Intern Med. 2006;166(11):1173–1177.
Vidyarthi AR, Arora V, Schnipper JL, Wall SD, Wachter RM. Managing discontinuity in academic medical centers: strategies for a safe and effective resident sign‐out. J Hosp Med. 2006;1(4):257–266.
Wilson RM, Runciman WB, Gibberd RW, Harrison BT, Newby L, Hamilton JD. The Quality in Australian Health Care Study. Med J Aust. 1995;163(9):458–471.
“Improving America's Hospitals.” The Joint Commission's Annual Report on Quality and Safety. Available at: http://www.jointcommission.org/Improving_Americas_Hospitals_The_Joint_Commissions_Annual_Report_on_Quality_and_Safety_‐_2007. Published 2007. Accessed November 17, 2014.
Arora V, Johnson J, Lovinger D, Humphrey HJ, Meltzer DO. Communication failures in patient sign‐out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005;14(6):401–407.
Myers JS, Bellini LM. Resident handoffs: appreciating them as a critical competency. J Gen Intern Med. 2012;27(3):270–272.
Accreditation Council for Graduate Medical Education. Common program requirements. Available at: http://www.acgme.org/acgmeweb/Portals/0/PDFs/Common_Program_Requirements_07012011%5B2%5D.pdf. Published July 1, 2011. Accessed November 17, 2014.
Joint Commission Center for Transforming Healthcare. Hand‐off communications. Available at: http://www.centerfortransforminghealthcare.org/projects/detail.aspx?Project=1. Accessed November 17, 2014.
Wacogne I, Diwakar V. Handover and note‐keeping: the SBAR approach. Clin Risk. 2010;16(5):173–175.
Chu ES, Reid M, Schulz T, et al. A structured handoff program for interns. Acad Med. 2009;84(3):347–352.
Starmer AJ, Spector ND, Srivastava R, et al. I‐pass, a mnemonic to standardize verbal handoffs. Pediatrics. 2012;129(2):201–204.
Haig KM, Sutton S, Whittington J. SBAR: a shared mental model for improving communication between clinicians. Jt Comm J Qual Patient Saf. 2006;32(3):167–175.
Li P, Ali S, Tang C, Ghali WA, Stelfox HT. Review of computerized physician handoff tools for improving the quality of patient care. J Hosp Med. 2013;8(8):456–463.
Starmer AJ, Spector ND, Srivastava R, et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014;371(19):1803–1812.
Fletcher KE, Davis SQ, Underwood W, Mangrulkar RS, McMahon LF, Saint S. Systematic review: effects of resident work hours on patient safety. Ann Intern Med. 2004;141(11):851–857.
Maggs F, Mallet M. Mortality in out‐of‐hours emergency medical admissions—more than just a weekend effect. J R Coll Physicians Edinb. 2010;40(2):115–118.
Khanna R, Wachsberg K, Marouni A, Feinglass J, Williams MV, Wayne DB. The association between night or weekend admission and hospitalization‐relevant patient outcomes. J Hosp Med. 2011;6(1):10–14.
Han PKJ, Klein WMP, Arora NK. Varieties of uncertainty in health care: a conceptual taxonomy. Med Decis Making. 2011;31(6):828–838.
Weick KE. Sensemaking in Organizations. Thousand Oaks, CA: Sage Publications; 1995.
McSweeney ME, Landrigan CP, Jiang H, Starmer A, Lightdale JR. Answering questions on call: pediatric resident physicians' use of handoffs and other resources. J Hosp Med. 2013;8(6):328–333.
Fogerty RL, Schoenfeld A, Salim Al‐Damluji M, Horwitz LI. Effectiveness of written hospitalist sign‐outs in answering overnight inquiries. J Hosp Med. 2013;8(11):609–614.
Fogerty RL, Rizzo TM, Horwitz LI. Assessment of internal medicine trainee sign‐out quality and utilization habits. Intern Emerg Med. 2014;9(5):529–535.
Devlin MK, Kozij NK, Kiss A, Richardson L, Wong BM. Morning handover of on‐call issues: opportunities for improvement. JAMA Intern Med. 2014;174(9):1479–1485.
Nabors C, Patel D, Khera S, et al. Improving resident morning sign‐out by use of daily events reports [published online ahead of print February 11, 2014]. J Patient Saf. doi: 10.1097/PTS.0b013e31829e4f56

References

Epstein K, Juarez E, Epstein A, Loya K, Singer A. The impact of fragmentation of hospitalist care on length of stay. J Hosp Med. 2010;5(6):335–358.
Horwitz LI, Krumholz HM, Green ML, Huot SJ. Transfers of patient care between house staff on internal medicine wards: a national survey. Arch Intern Med. 2006;166(11):1173–1177.
Vidyarthi AR, Arora V, Schnipper JL, Wall SD, Wachter RM. Managing discontinuity in academic medical centers: strategies for a safe and effective resident sign‐out. J Hosp Med. 2006;1(4):257–266.
Wilson RM, Runciman WB, Gibberd RW, Harrison BT, Newby L, Hamilton JD. The Quality in Australian Health Care Study. Med J Aust. 1995;163(9):458–471.
“Improving America's Hospitals.” The Joint Commission's Annual Report on Quality and Safety. Available at: http://www.jointcommission.org/Improving_Americas_Hospitals_The_Joint_Commissions_Annual_Report_on_Quality_and_Safety_‐_2007. Published 2007. Accessed November 17, 2014.
Arora V, Johnson J, Lovinger D, Humphrey HJ, Meltzer DO. Communication failures in patient sign‐out and suggestions for improvement: a critical incident analysis. Qual Saf Health Care. 2005;14(6):401–407.
Myers JS, Bellini LM. Resident handoffs: appreciating them as a critical competency. J Gen Intern Med. 2012;27(3):270–272.
Accreditation Council for Graduate Medical Education. Common program requirements. Available at: http://www.acgme.org/acgmeweb/Portals/0/PDFs/Common_Program_Requirements_07012011%5B2%5D.pdf. Published July 1, 2011. Accessed November 17, 2014.
Joint Commission Center for Transforming Healthcare. Hand‐off communications. Available at: http://www.centerfortransforminghealthcare.org/projects/detail.aspx?Project=1. Accessed November 17, 2014.
Wacogne I, Diwakar V. Handover and note‐keeping: the SBAR approach. Clin Risk. 2010;16(5):173–175.
Chu ES, Reid M, Schulz T, et al. A structured handoff program for interns. Acad Med. 2009;84(3):347–352.
Starmer AJ, Spector ND, Srivastava R, et al. I‐pass, a mnemonic to standardize verbal handoffs. Pediatrics. 2012;129(2):201–204.
Haig KM, Sutton S, Whittington J. SBAR: a shared mental model for improving communication between clinicians. Jt Comm J Qual Patient Saf. 2006;32(3):167–175.
Li P, Ali S, Tang C, Ghali WA, Stelfox HT. Review of computerized physician handoff tools for improving the quality of patient care. J Hosp Med. 2013;8(8):456–463.
Starmer AJ, Spector ND, Srivastava R, et al. Changes in medical errors after implementation of a handoff program. N Engl J Med. 2014;371(19):1803–1812.
Fletcher KE, Davis SQ, Underwood W, Mangrulkar RS, McMahon LF, Saint S. Systematic review: effects of resident work hours on patient safety. Ann Intern Med. 2004;141(11):851–857.
Maggs F, Mallet M. Mortality in out‐of‐hours emergency medical admissions—more than just a weekend effect. J R Coll Physicians Edinb. 2010;40(2):115–118.
Khanna R, Wachsberg K, Marouni A, Feinglass J, Williams MV, Wayne DB. The association between night or weekend admission and hospitalization‐relevant patient outcomes. J Hosp Med. 2011;6(1):10–14.
Han PKJ, Klein WMP, Arora NK. Varieties of uncertainty in health care: a conceptual taxonomy. Med Decis Making. 2011;31(6):828–838.
Weick KE. Sensemaking in Organizations. Thousand Oaks, CA: Sage Publications; 1995.
McSweeney ME, Landrigan CP, Jiang H, Starmer A, Lightdale JR. Answering questions on call: pediatric resident physicians' use of handoffs and other resources. J Hosp Med. 2013;8(6):328–333.
Fogerty RL, Schoenfeld A, Salim Al‐Damluji M, Horwitz LI. Effectiveness of written hospitalist sign‐outs in answering overnight inquiries. J Hosp Med. 2013;8(11):609–614.
Fogerty RL, Rizzo TM, Horwitz LI. Assessment of internal medicine trainee sign‐out quality and utilization habits. Intern Emerg Med. 2014;9(5):529–535.
Devlin MK, Kozij NK, Kiss A, Richardson L, Wong BM. Morning handover of on‐call issues: opportunities for improvement. JAMA Intern Med. 2014;174(9):1479–1485.
Nabors C, Patel D, Khera S, et al. Improving resident morning sign‐out by use of daily events reports [published online ahead of print February 11, 2014]. J Patient Saf. doi: 10.1097/PTS.0b013e31829e4f56

Issue

Journal of Hospital Medicine - 10(3)

Issue

Journal of Hospital Medicine - 10(3)

Page Number

142-146

Page Number

142-146

Publications

Publications

Article Type

Display Headline

Nighttime clinical encounters: How residents perceive and respond to calls at night

Display Headline

Nighttime clinical encounters: How residents perceive and respond to calls at night

Sections

Article Source

Disallow All Ads

Corresponding Author

Correspondence Location

Address for correspondence and reprint requests: Joshua T. Hanson, MD, 7703 Floyd Curl Drive, MC 7982, San Antonio, TX 78229‐3900; Telephone: 415‐297‐7487; Fax: 210–358‐0647; E‐mail: hansonj4@uthscsa.edu

Content Gating

No Gating (article Unlocked/Free)

Alternative CME

Article PDF Media

Media Files

Housestaff Teams and Patient Outcomes

Article Type

Changed

Sun, 05/21/2017 - 13:37

Display Headline

Relationships within inpatient physician housestaff teams and their association with hospitalized patient outcomes

Author(s)

Caitlin McAllister, MD

Heather Schacht Reisinger, PhD

Holly Lanham, PhD

Michael Parchman, MD, MPH

Jacqueline Pugh, MD

Since the Institute of Medicine Report To Err is Human, increased attention has been paid to improving the care of hospitalized patients.[1] Strategies include utilization of guidelines and pathways, and the application of quality improvement techniques to improve or standardize processes. Despite improvements in focused areas such as prevention of hospital‐acquired infections, evidence suggests that outcomes for hospitalized patients remain suboptimal.[2] Rates of errors and hospital‐related complications such as falls, decubitus ulcers, and infections remain high,[3, 4, 5] and not all patients receive what is known to be appropriate care.[6]

Many attempts to improve inpatient care have used process‐improvement approaches, focusing on impacting individuals' behaviors, or on breaking down processes into component parts. Examples include central line bundles or checklists.[7, 8] These approaches attempt to ensure that providers do things in a standardized way, but are implicitly based on the reductionist assumption that we can break processes down into predictable parts to improve the system. An alternative way to understand clinical systems is based on interdependencies between individuals in the system, or the ways in which parts of the system interact with each other, which may be unpredictable over time.[1, 9] Whereas these interdependencies include care processes, they also encompass the providers who care for patients. Providers working together vary in terms of the kinds of relationships they have with each other. Those relationships are crucial to system function because they are the foundation for the interactions that lead to effective patient care.

The application of several frameworks or approaches for considering healthcare systems in terms of relationships highlights the importance of this way of understanding system function. The include complexity science,[1, 7] relational coordination (which is grounded in complexity science),[10] high reliability,[11] and the Big Five for teamwork.[12]

Research indicates that interactions among healthcare providers can have important influences on outcomes.[13, 14, 15, 16, 17] Additionally, the initial implementation of checklists to prevent central‐line associated infections appeared to change provider relationships in a way that significantly influenced their success.[18] For example, positive primary care clinic member relationships as assessed by the Lanham framework have been associated with better chronic care model implementation, learning, and patient experience of care.[19, 20] This framework, which we apply here, identifies 7 relationship characteristics: (1) trust; (2) diversity; (3) respect; (4) mindfulness, or being open to new ideas from others; (5) heedfulness, or an understanding of how one's roles influence those of others; (6) use of rich in‐person or verbal communication, particularly for potentially ambiguous information open to multiple interpretations; and (7) having a mixture of social and task relatedness among teams, or relatedness outside of only work‐related tasks.[19] Relationships within surgical teams that are characterized by psychological safety and diversity are associated with successful uptake of new techniques and decreased mortality.[13, 14] Relationships are important because the ability of patients and providers to learn and make sense of their patients' illnesses is grounded in relationships.

We sought to better understand and characterize inpatient physician teams' relationships, and assess the association between team relationships as evaluated by Lanham's framework and outcomes for hospitalized patients. Data on relationships among inpatient medical teams are few, despite the fact that these teams provide a great proportion of inpatient care. Additionally, the care of hospitalized medical patients is complex and uncertain, often involving multiple providers, making provider relationships potentially even more important to outcomes than in other settings.

METHODS

Overview

We conducted an observational, convergent mixed‐methods study of inpatient medicine teams.[21, 22, 23] We focused on inpatient physician teams, defining them as the functional work group responsible for medical decision making in academic medical centers. Physician teams in this context have been studied in terms of social hierarchy, authority, and delegation.[24, 25, 26] Focusing on the relationships within these groups could provide insights into strategies to mitigate potential negative effects of hierarchy. We recognize that other providers are closely involved in the care of hospitalized patients, and although we did not have standard interactions between physicians, nurses, case managers, and other providers that we could consistently observe, we did include interactions with these other providers in our observations and assessments of team relationships. Because this work is among the first in inpatient medical teams, we chose to study a small number of teams in great depth, allowing us to make rich assessments of team relationships.

We chose patient outcomes of length of stay (LOS), unnecessary LOS (ULOS), and complication rates, adjusted for patient characteristics and team workload. LOS is an important metric of inpatient care delivery. We feel ULOS is an aspect of LOS that is dependent on the physician team, as it reflects their preparation of the patient for discharge. Finally, we chose complication rates because hospital‐acquired conditions and complications are important contributors to inpatient morbidity, and because recent surgical literature has identified complication rates as a contributor to mortality that could be related to providers' collective ability to recognize complications and act quickly.

This study was approved by the institutional review board at the University of Texas Health Science Center at San Antonio (UTHSCSA), the Research and Development Committee for the South Texas Veterans Health Care System (STVHCS), and the Research Committee at University Health System (UHS). All physicians consented to participate in the study. We obtained a waiver of consent for inclusion of patient data.

Setting and Study Participants

This study was conducted at the 2 UTHSCSA primary teaching affiliates. The Audie L. Murphy Veterans Affairs Hospital is the 220‐bed acute‐care hospital of the STVHCS. University Hospital is the 614‐bed, level‐I trauma, acute‐care facility for UHS, the county system for Bexar County, which includes the San Antonio, Texas major metropolitan area.

The inpatient internal medicine physician team was our unit of study. Inpatient medicine teams consisted of 1 faculty attending physician, 1 postgraduate year (PGY)‐2 or PGY‐3 resident, and 2 PGY‐1 members. In addition, typically 2 to 3 third‐year medical students were part of the team, and a subintern was sometimes present. Doctor of Pharmacy faculty and students were also occasionally part of the team. Social workers and case managers often joined team rounds for portions of the time, and nurses sometimes joined bedside rounds on specific patients. These teams admit all medicine patients with the exception of those with acute coronary syndromes, new onset congestive heart failure, or arrhythmias. Patients are randomly assigned to teams based on time of admission and call schedules.

Between these 2 hospitals, there are 10 inpatient medicine teams caring for patients, with a pool of over 40 potential faculty attendings. Our goal was to observe teams that would be most likely to vary in terms of their relationship characteristics and patient outcomes through observing teams with a range of individual members. We used a purposeful sampling approach to obtain a diverse sample, sampling based on physician attributes and time of year.[16, 17] Three characteristics were most important: attending physician years of experience, attending involvement in educational and administrative leadership, and the presence of struggling resident members, as defined by being on probation or having been discussed in the residency Clinical Competency Committee. We did not set explicit thresholds in terms of attending experience, but instead sought to ensure a range. The attendings we observed were more likely to be involved in education and administrative leadership activities, but were otherwise similar to those we did not observe in terms of years of experience. We included struggling residents to observe individuals with a range of skill sets, and not just high‐performing individuals. We obtained attending information based on our knowledge of the attending faculty pool, and from the internal medicine residency program. We sampled across the year to ensure a diversity of trainee experience, but did not observe teams in either July or August, as these months were early in the academic year. Interns spend approximately 5 months per year on inpatient services, whereas residents spend 2 to 3 months per year. Thus, interns but not residents observed later in the year might have spent significantly more time on an inpatient service. However, in all instances, none of the team members observed had worked together previously.

Data Collection

Data were collected over nine 1‐month periods from September 2008 through June 2011. Teams were observed daily for 2‐ to 4‐week periods during morning rounds, the time when the team discusses each patient and makes clinical decisions. Data collection started on the first day of the month, the first day that all team members worked together, and continued for approximately 27 days, the last day before the resident rotated to a different service. By comprehensively and systematically observing these teams' daily rounds, we obtained rich, in‐depth data with multiple data points, enabling us to assess specific team behaviors and interactions.

During the third and fourth months, we collected data on teams in which the attending changed partway through. We did this to understand the impact of individual attending change on team relationships. Because the team relationships differed with each attending, we analyzed them separately. Thus, we observed 7 teams for approximately 4‐week periods and 4 teams for approximately 2‐week periods.

Observers arrived in the team room prior to rounds to begin observations, staying until after rounds were completed. Detailed free‐text field notes were taken regarding team activities and behaviors, including how the teams made patient care decisions. Field notes included: length of rounds, which team members spoke during each patient discussion, who contributed to management discussions, how information from consultants was incorporated, how communication with others outside of the team occurred, how team members spoke with each other including the types of words used, and team member willingness to perform tasks outside of their usually defined role, among others. Field notes were collected in an open‐ended format to allow for inductive observations. Observers also recorded clinical data daily regarding each patient, including admission and discharge dates, and presenting complaint.

The observation team consisted of the principle investigator (PI) (hospitalist) and 2 research assistants (a graduate‐level medical anthropologist and social psychologist), all of whom were trained by a qualitative research expert to systematically collect data related to topics of interest. Observers were instructed to record what the teams were doing and talking about at all times, noting any behaviors that they felt reflected how team members related to each other and came to decisions about their patients, or that were characteristic of the team. To ensure consistency, the PI and 1 research assistant conducted observations jointly at the start of data collection for each team, checking concordance of observations daily using a percent agreement until general agreement on field note content and patient information reached 90%. Two individuals observed 24 days of data collection, representing 252 patient discussions (13% of observed discussions).

An age‐adjusted Charlson‐Deyo comorbidity score was calculated for each patient admitted to each team, using data from rounds and from each hospital's electronic health records (EHR).[27] We collected data regarding mental health conditions for each patient (substance use, mood disorder, cognitive disorder, or a combination) because these comorbidities could impact LOS or ULOS. Discharge diagnoses were based on the discharge summary in the EHR. We also collected data daily regarding team census and numbers of admissions to and discharges from each team to assess workload.

Three patient outcomes were measured: LOS, ULOS, and complications. LOS was defined as the total number of days the patient was in the hospital. ULOS was defined as the number of days a patient remained in the hospital after the day the team determined the patient was medically ready for discharge (assessed by either discussion on rounds or EHR documentation). ULOS may occur when postdischarge needs have been adequately assessed, or because of delays in care, which may be related to provider communication during the hospitalization. Complications were defined on a per‐patient, per‐day basis in 2 ways: the development of a new problem in the hospital, for example acute kidney injury, a hospital‐acquired infection, or delirium, or by the team noting a clinical deterioration after at least 24 hours of clinical stability, such as the patient requiring transfer to a higher level of care. Complications were determined based on discussions during rounds, with EHR verification if needed.

Analysis Phase I: Assessment of Relationship Characteristics

After the completion of data collection, field notes were reviewed by a research team member not involved in the original study design or primary data collection (senior medical student). We took this approach to guard against biasing the reviewer's view of team behaviors, both in terms of not having conducted observations of the teams and being blinded to patient outcomes.

The reviewer completed a series of 3 readings of all field notes. The first reading provided a summary of the content of the data and the individual teams. Behavioral patterns of each team were used to create an initial team profile. The field notes and profiles were reviewed by the PI and a coauthor not involved in data collection to ensure that the profiles adequately reflected the field notes. No significant changes to the profiles were made based on this review. The profiles were discussed at a meeting with members of the larger research team, including the PI, research assistants, and coinvestigators (with backgrounds in medicine, anthropology, and information and organization management). Behavior characteristics that could be used to distinguish teams were identified in the profiles using a grounded theory approach.

The second review of field notes was conducted to test the applicability of the characteristics identified in the first review. To systematically record the appearance of the behaviors, we created a matrix with a row for each behavior and columns for each team to note whether they exhibited each behavior. If the behavior was exhibited, specific examples were cataloged in the matrix. This matrix was reviewed and refined by the research team. During the final field note review meeting, the research team compared the summary matrix for each team, with the specific behaviors noted during the first reading of the field notes to ensure that all behaviors were recorded.

After cataloging behaviors, the research team assigned each behavior to 1 of the 7 Lanham relationship characteristics. We wanted to assess our observations against a relationship framework to ensure that we were able to systematically assess all aspects of relationships. The Lanham framework was initially developed based on a systematic review of the organizational and educational literatures, making it relevant to the complex environment of an academic medical inpatient team and allowing us to assess relationships at a fine‐grained, richly detailed level. This assignment was done by the author team as a group. Any questions were discussed and different interpretations resolved through consensus. The Lanham framework has 7 characteristics.[19] Based on the presence of behaviors associated with each relationship characteristic, we assigned a point to each team for each relationship characteristic observed. We considered a behavior type to be present if we observed it on at least 3 occasions on separate days. Though we used a threshold of at least 3 occurrences, most teams that did not receive a point for a particular characteristic did not have any instances in which we observed the characteristic. This was particularly true for trust and mindfulness, and least so for social/task relatedness. By summing these points, we calculated a total relationship score for each team, with potential scores ranging from 0 (for teams exhibiting no behaviors reflecting a particular relationship characteristic) to 7.

Analysis Phase II: Factor Analysis

To formally determine which relationship characteristics were most highly related, data were submitted to a principal components factor analysis using oblique rotation. Item separation was determined by visual inspection of the scree plot and eigenvalues over 1.

Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes

We examined the association between team relationships and patient outcomes using team relationship scores. For the LOS/ULOS analysis, we only included patients whose entire hospitalization occurred under the care of the team we observed. Patients who were on the team at the start of the month, were transferred from another service, or who remained hospitalized after the end of the team's time together were excluded. The longest possible LOS for patients whose entire hospitalization occurred on teams that were observed for half a month was 12 days. To facilitate accurate comparison between teams, we only included patients whose LOS was 12 days.

Complication rates were defined on a per‐patient per‐day basis to normalize for different team volumes and days of observation. For this analysis, we included patients who remained on the team after data collection completion, patients transferred to another team, or patients transferred from another team. However, we only counted complications that occurred at least 24 hours following transfer to minimize the likelihood that the complication was related to the care of other physicians.

Preliminary analysis involved inspection and assessment of the distribution of all variables followed, by a general linear modeling approach to assess the association between patient and workload covariates and outcomes.[28, 29] Because we anticipated that outcome variables would be markedly skewed, we also planned to assess the association between relationship characteristics with outcomes using the Kruskal‐Wallis rank sum test to compare groups with Dunn's test[30] for pairwise comparisons if overall significance occurred.[31] There are no known acceptable methods for covariate adjustments using the Kruskal‐Wallis method. All models were run using SAS software (SAS Institute Inc., Cary, NC).[32]

RESULTS

The research team observed 1941 discussions of 576 individual patients. Observations were conducted over 352 hours and 54 minutes, resulting in 741 pages of notes (see Supporting Table 1 in the online version of this article for data regarding individual team members). Teams observed over half‐months are referred to with a and b designations.

Table 1.Relationship Characteristics and Observed Behaviors
Relationship Characteristic	Definition	Thirteen Types of Behaviors Observed in Field Notes	Observed Examples
Trust	Willingness to be vulnerable to others	Use of we instead of you or I by the attending	Where are we going with this guy?
Trust	Willingness to be vulnerable to others	Attending admitting I don't know	Let's go talk to him, I can't figure this out
Asking questions to help team members to think through problems	Will the echo change our management? How will it help us?
Diversity	Including different perspectives and different thinking	Team member participation in conversations about patients that are not theirs	One intern is presenting, another intern asks a question, and the resident joins the discussion
Diversity	Including different perspectives and different thinking	Inclusion of perspectives of those outside the team (nursing and family members)	Taking a break to call the nurse, having a family meeting
Respect	Valuing the opinions of others, honest and tactful interactions	Use of positive reinforcement by the attending	Being encouraging of the medical student's differential, saying excellent
Respect		How the team talks with patients	Asking if the patient has any concerns, what they can do to make them comfortable
Heedfulness	Awareness of how each person's roles impact the rest of the team	Team members performing tasks not expected of their role	One intern helping another with changing orders to transfer a patient
Heedfulness		Summarizing plans and strategizing	Attending recaps the plan for the day, asks what they can do
Mindfulness	Openness to new ideas/free discussion about what is and is not working	Entire team engaged in discussion	Attending asks the medical student, intern, and resident what they think is going on
Social relatedness	Having socially related interactions	Social conversation among team members	Intern talks about their day off
Social relatedness	Having socially related interactions	Jokes by the attending	Showers and a bowel movement is the key to making people happy
Appropriate use of rich communication	Use of in‐person communication for sensitive or difficult issues	Using verbal communication with consultants or family	Intern is on the phone with the pharm D because there is a problem with the medication

Creation of team profiles yielded 13 common behavior characteristics that were inductively identified and that could potentially distinguish teams, including consideration of perspectives outside of the team and team members performing tasks normally outside of their roles. Table 1 provides examples of and summarizes observed behaviors using examples from the field notes, mapping these behavior characteristics onto the Lanham relationship characteristics. The distribution of relationship characteristics and scores for each team are shown in Table 2.

Table 2.Team Relationship Profiles
Relationship Characteristic	Team
Relationship Characteristic	1	2	3a	3b	4a	4b	5	6	7	8	9
Trust	0	0	1	0	0	0	1	0	1	1	1
Diversity	0	1	1	1	0	0	0	0	1	1	1
Respect	0	1	1	1	0	1	0	0	1	1	1
Heedfulness	0	1	1	0	1	0	1	0	1	1	1
Mindfulness	0	0	1	0	0	1	1	0	1	1	1
Social/task relatedness	0	1	1	0	1	1	1	0	1	1	1
Rich/lean communication	0	1	1	0	0	0	1	0	1	1	0
Relationship score (no. of characteristics observed)	0	5	7	2	2	3	5	0	7	7	6

Correlation between relationship characteristics ranged from 0.32 to 0.95 (see Supporting Table 2 in the online version of this article). Mindfulness and trust are more highly correlated with each other than with other variables, as are diversity and respect. We performed a principal components factor analysis. Based on scree plot inspection and eigenvalues >1, we kept 3 factors that explained 85% of the total variance (see Supporting Table 3 in the online version of this article).

Table 3.Association Between the Teams' Number of Relationship Characteristics and Patient Outcomes
	No. of Relationship Characteristics
	02	35	67
NOTE: Abbreviations: IQR, interquartile range; LOS, length of stay; ULOS, unnecessary length of stay. a Not significant.
LOS, d, n=293
Median	4	5	3
IQR	5	4	3
Mean	4.7 (2.72)	4.7 (2.52)	4.1 (2.51), P=0.12a
ULOS, d, n=293
Median	0	0	0
IQR	0	0	0
Mean	0.37 (0.99)	0.33 (0.96)	0.13 (0.56), P=0.09a
Complications (per patient per day), n=398
Median	0	0	0
IQR	1	1	0
Mean	0.58 (1.06)	0.45 (0.77)	0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics

Our analyses of LOS and ULOS included 298 of the 576 patients. Two hundred sixty‐seven patients were excluded because their entire LOS did not occur while under the care of the observed teams. Eleven patients were removed from the analysis because their LOS was >12 days. The analysis of complications included 398 patients. In our preliminary general linear modeling approach, only patient workload was significantly associated with outcomes using a cutoff of P=0.05. Charlson‐Deyo score and mental health comorbidities were not associated with outcomes.

The results of the Kruskal‐Wallis test show the patient average ranking on each of the outcome variables by 3 groups (Table 3). Overall, teams with higher relationship scores had lower rank scores on all outcomes measures. However, the only statistically significant comparisons were for complications. Teams having 6 to 7 characteristics had a significantly lower complication rate ranking than teams with 0 to 2 and 3 to 5 (P=0.001). We did not find consistent differences between individual teams or groups of teams with relationship scores from 0 to 2, 3 to 5, and 6 to 7 with regard to Charlson score, mental health issues, or workload. The only significant differences were between Charlson‐Deyo scores for patients admitted to teams with low relationship scores of 0 to 2 versus high relationship scores of 6 to 7 (6.7 vs 5.1); scores for teams with relationship scores of 3 to 5 were not significantly different from the low or high groups.

Table 4 shows the Kruskal‐Wallace rank test results for each group of relationship characteristics identified in the factor analysis based on whether teams displayed all or none of the characteristics in the factor. There were no differences in these groupings for LOS. Teams that exhibited both mindfulness and trust had lower ranks on ULOS than teams that did not have either. Similarly, teams with heedfulness, social‐task relatedness, and more rich communication demonstrated lower ULOS rankings than teams who did not have all 3 characteristics.

Table 4.Association Between Inpatient Physician Team Relationship Characteristics and Outcomes
	Mind/Trust		Diversity/Respect		Heed/Relate/Communicate
Patient Outcome	None	Both	None	Both	None	All 3
NOTE: Abbreviations: IQR, interquartile range; LOS, length of stay; ULOS, unnecessary length of stay. a Not significant.
LOS, d, n=293
Median	4	4	4	4	4	4
IQR	5	3	4.5	3	4	4
Mean	4.7 (2.6)	4.2 (2.5)	4.7 (2.6)	4.3 (2.5)	4.4 (2.6)	4.4 (2.6)
P value	0.06a		0.23a		0.85a
ULOS, d, n=293
Median	0	0	0	0	0	0
IQR	0	0	0	0	0	0
Mean	0.39 (1.01)	0.15 (0.62)	0.33 (0.92)	0.18 (0.71)	0.32 (0.93)	0.18 (0.69)
P value	0.009		0.06		0.03
Complications (per patient), n=389
Median	0	0	0	0	0	0
IQR	1	0	1	0	1	0
Mean	0.58 (1.01)	0.19 (0.58)	0.47 (0.81)	0.29 (0.82)	0.26 (0.92)	0.28 (0.70)
P value	<0.0001		0.001		0.02

DISCUSSION

Relationships are critical to team function because they are the basis for the social interactions that are central to patient care. These interactions include how providers recognize and make sense of what is happening with patients, and how they learn to care for patients more effectively. Additionally, the high task interdependencies among inpatient providers require effective relationships for optimal care. In our study, inpatient medicine physician teams' relationships varied, and these differences were associated with ULOS and complications. Relationship characteristics are not mutually exclusive, and as our factor analysis demonstrates, are intercorrelated. Trust and mindfulness appear to be particularly important. Trust may foster psychological safety that in turn promotes the willingness of individuals to contribute their thoughts and ideas.[13] In low‐trust teams, providers may fear a negative impact for bringing forward a concern based on limited data. Mindful teams may be more likely to notice nuanced changes, or are more likely to talk when things just do not appear to be going in the right direction with the patient. In the case of acutely ill medical patients, trust and mindfulness may lead to an increased likelihood that clinical changes are recognized and discussed quickly. For example, on a team characterized by trust and mindfulness, the entire team was typically involved in care discussions, and the interns and students frequently asked a lot of questions, even regarding the care of patients they were not directly following. We observed that these questions and discussions often led the team to realize that they needed to make a change in management decisions (eg, discontinuing Bactrim, lowering insulin doses, adjusting antihypertensives, premedicating for intravenous contrast) that they had not caught in the assessment and plan portion of the patient care discussion. In another example, a medical student asked a tentative question after a patient needed to go quickly to the bathroom while they were examining her, leading the team to ask more questions that led to a more rapid evaluation of a potential urinary tract infection. This finding is consistent with the description of failure to rescue among surgical patients, in which mortality has been associated with the failure to recognize complications rapidly and act effectively.[33]

Our findings are limited in several ways. First, these data are from a single academic institution. Although we sought diversity among our teams and collected data across 2 hospitals, there may be local contextual factors that influenced our results. Second, our data demonstrate an association, but not causality. Our findings should be tested in studies that assess causality and potential mechanisms through which relationships influence outcomes. Third, the individuals observing the teams had some knowledge of patient outcomes through hearing patient discussions. However, by involving individuals who did not participate in observations and were blinded to outcomes in assessing team relationships, we addressed this potential bias. Fourth, our observations were largely focused on physician teams, not directly including other providers. Our difficulty in observing regular interactions between physicians and other providers underscores the need to increase contact among those caring for hospitalized patients, such as occurs through multidisciplinary rounds. We did include team communication with other disciplines in our assessment of the relationship characteristics of diversity and rich communication. Finally, our analysis was limited by our sample size. We observed a relatively small number of teams. Although we benefitted from seeing the change in team relationships that occurred with attending changes halfway through some of our data collection months, this did limit the number of patients we could include in our analyses. Though we did not observe obvious differences in relationships between the teams observed across the 2 hospitals, the small number of teams and hospitals precluded our ability to perform multilevel modeling analyses, which would have allowed us to assess or account for the influence of team or organizational factors. However, this small sample size did allow for a richer assessment of team behaviors.

Although preliminary, our findings are an important step in understanding the function of inpatient medical teams not only in terms of processes of care, but also in terms of relationships. Patient care is a social activity, requiring effective communication to develop working diagnoses, recognize changes in patients' clinical courses, and formulate effective treatment plans during and after hospitalization. Future work could follow several directions. One would be to assess the causal mechanisms through which relationships influence patient outcomes. These may include sensemaking, learning, and improved coordination. Positive relationships may facilitate interaction of tacit and explicit information, facilitating the creation of understandings that foster more effective patient care.[34] The dynamic nature of relationships and how patient outcomes in turn feed back into relationships could be an area of exploration. This line of research could build on the idea of teaming.[35] Understanding relationships across multidisciplinary teams or with patients and families would be another direction. Finally, our results could point to potential interventions to improve patient outcomes through improving relationships. Better understanding of the nature of effective relationships among providers should enable us to develop more effective strategies to improve the care of hospitalized patients. In the larger context of payment reforms that require greater coordination and communication among and across providers, a greater understanding of how relationships influence patient outcomes will be important.

Acknowledgements

The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.

Disclosures: The research reported herein was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CDA 07‐022). Investigator salary support was provided through this funding, and through the South Texas Veterans Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. Dr. McDaniel receives support from the IC[2] Institute of the University of Texas at Austin. Dr. Luci Leykum had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.

Files

References

Plsek P. Redesigning health care with insights from the science of complex adaptive systems. In: Crossing the Quality Chasm: A New Heath System for the 21st Century. Washington, DC: National Academy of Sciences; 2000:309–322.
Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;323(22):2124–2135.
Krauss MJ, Nguyen SL, Dunagan WC, et al. Circumstances of patient falls and injuries in 9 hospitals in a mid‐western healthcare system. Infect Control Hosp Epidemiol. 2007;28(5):544–550.
Hurd T, Posnett J. Point prevalence of wounds in a sample of acute hospitals in Canada. Int Wound J. 2009;6(4):287–293.
Garcin F, Leone M, Antonini F, Charvet A, Albanese J, Martin C. Non‐adherence to guidelines: an avoidable cause of failure of empirical antimicrobial therapy in the presence of difficult‐to‐treat bacteria. Intensive Care Med. 2010;36(1):75–82.
Williams SC, Schmaltz SP, Morton DJ, Koss RG, Loeb JM. Quality of care in U.S. hospitals as reflected by standardized measures, 2002–2004. N Engl J Med. 2005;353(3):255–264.
Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases. Division of Healthcare Quality Promotion. Checklist for prevention of central line associated blood stream infections. Available at: http://www.cdc.gov/HAI/pdfs/bsi/checklist‐for‐CLABSI.pdf. Accessed August 3, 2014.
Safer Healthcare Partners, LLC. Checklists: a critical patient safety tool. Available at: http://www.saferhealthcare.com/high‐reliability‐topics/checklists. Accessed July 31, 2014.
Yam Y. Making Things Work: Solving Complex Problems in a Complex World. Boston, MA: Knowledge Press; 2004:117–160.
Gittell JH. High Performance Healthcare: Using The Power of Relationships to Achieve Quality, Efficiency, and Resilience. 1st ed. New York, NY: McGraw‐Hill; 2009.
Carroll JS, Rudolph JW. Design of high reliability organizations in health care. Qual Saf Health Care. 2006;15(suppl 1):i4–i9.
Salas E, DiazGranados D, Weaver SJ, King H. Does team training work? Principles for health care. Acad Emerg Med. 2008;15(11):1002–1009.
Edmondson A. Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manag Stud. 2003;40(6):1419–1452.
Neily J, Mills PD, Young‐Xu Y, et al. Association between implementation of a medical team training program and surgical mortality. JAMA. 2010;304(15):1693–1700.
Lewis K, Belliveau M, Herndon B, Keller J. Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge. Organ Behav Hum Decis Process. 2007;103(2):159–178.
Leykum LK, Palmer RF, Lanham HJ, McDaniel RR, Noel PH, Parchman ML. Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care settings. BMC Health Serv Res. 2011;11:44.
Noel PH, Lanham HJ, Palmer RF, Leykum LK, Parchman ML. The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manage Rev. 2012;38(1):20–28.
Dixon‐Woods M, Bosk CL, Aveling EL, Goeschel CA, Pronovost PJ. Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.
Lanham HJ, McDaniel RR, Crabtree BF, et al. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457–466.
Finely EP, Pugh JA, Lanham HJ, et al. Relationship quality and patient‐assessed quality of care in VA primary care clinics: development and validation of the work relationships scale. Ann Fam Med. 2013;11(6):543–549.
Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. 2nd ed. Thousand Oaks, CA: Sage; 2011.
Patton MQ. Qualitative Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
Pope C, Royen P, Baker R. Qualitative methods in research on health care quality. Qual Saf Health Care. 2002;11:148–152.
Hoff T. Managing the negatives of experience in physician teams. Health Care Manage Rev. 2010;35(1):65–76.
Tamuz M, Giardina TD, Thomas EJ, Menon S, Singh H. Rethinking resident supervision to improve safety: from hierarchical to interprofessional models. J Hosp Med. 2011;6(8):445 b452.
Klein KJ, Ziegart JC, Knight AP, Xiao Y. Dynamic delegation: shared, hierarchical, and deindividualized leadership in extreme action teams. Adm Sci Q. 2006;51(4):590–621.
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
Tukey JW. Exploratory Data Analysis. Reading, MA: Addison‐Wesley; 1977.
Zar JH. Biostatistical Analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice‐Hall; 2010.
Dunn OJ. Multiple contrasts using rank sums. Technometrics. 1964;6:241–252.
Elliott AC, Hynan LS. A SAS macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Comput Methods Programs Biomed. 2011;102:75–80.
SAS/STAT Software [computer program]. Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029–1034.
Nonaka I. A dynamic theory of organizational knowledge creation. Org Sci. 1994;5(1):14–37.
Edmundson AC. Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. 1st ed. Boston, MA: Harvard Business School; 2012.

Article PDF

Issue

Journal of Hospital Medicine - 9(12)

Publications

Page Number

764-771

Read more about Housestaff Teams and Patient Outcomes

Sections

Author(s)

Caitlin McAllister, MD

Heather Schacht Reisinger, PhD

Holly Lanham, PhD

Michael Parchman, MD, MPH

Jacqueline Pugh, MD

Author(s)

Caitlin McAllister, MD

Heather Schacht Reisinger, PhD

Holly Lanham, PhD

Michael Parchman, MD, MPH

Jacqueline Pugh, MD

Files

Files

Article PDF

Article PDF

METHODS

Overview

Setting and Study Participants

Data Collection

Analysis Phase I: Assessment of Relationship Characteristics

Analysis Phase II: Factor Analysis

Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes

RESULTS

Table 1.Relationship Characteristics and Observed Behaviors
Relationship Characteristic	Definition	Thirteen Types of Behaviors Observed in Field Notes	Observed Examples
Trust	Willingness to be vulnerable to others	Use of we instead of you or I by the attending	Where are we going with this guy?
Trust	Willingness to be vulnerable to others	Attending admitting I don't know	Let's go talk to him, I can't figure this out
Asking questions to help team members to think through problems	Will the echo change our management? How will it help us?
Diversity	Including different perspectives and different thinking	Team member participation in conversations about patients that are not theirs	One intern is presenting, another intern asks a question, and the resident joins the discussion
Diversity	Including different perspectives and different thinking	Inclusion of perspectives of those outside the team (nursing and family members)	Taking a break to call the nurse, having a family meeting
Respect	Valuing the opinions of others, honest and tactful interactions	Use of positive reinforcement by the attending	Being encouraging of the medical student's differential, saying excellent
Respect		How the team talks with patients	Asking if the patient has any concerns, what they can do to make them comfortable
Heedfulness	Awareness of how each person's roles impact the rest of the team	Team members performing tasks not expected of their role	One intern helping another with changing orders to transfer a patient
Heedfulness		Summarizing plans and strategizing	Attending recaps the plan for the day, asks what they can do
Mindfulness	Openness to new ideas/free discussion about what is and is not working	Entire team engaged in discussion	Attending asks the medical student, intern, and resident what they think is going on
Social relatedness	Having socially related interactions	Social conversation among team members	Intern talks about their day off
Social relatedness	Having socially related interactions	Jokes by the attending	Showers and a bowel movement is the key to making people happy
Appropriate use of rich communication	Use of in‐person communication for sensitive or difficult issues	Using verbal communication with consultants or family	Intern is on the phone with the pharm D because there is a problem with the medication

Table 2.Team Relationship Profiles
Relationship Characteristic	Team
Relationship Characteristic	1	2	3a	3b	4a	4b	5	6	7	8	9
Trust	0	0	1	0	0	0	1	0	1	1	1
Diversity	0	1	1	1	0	0	0	0	1	1	1
Respect	0	1	1	1	0	1	0	0	1	1	1
Heedfulness	0	1	1	0	1	0	1	0	1	1	1
Mindfulness	0	0	1	0	0	1	1	0	1	1	1
Social/task relatedness	0	1	1	0	1	1	1	0	1	1	1
Rich/lean communication	0	1	1	0	0	0	1	0	1	1	0
Relationship score (no. of characteristics observed)	0	5	7	2	2	3	5	0	7	7	6

Table 3.Association Between the Teams' Number of Relationship Characteristics and Patient Outcomes
	No. of Relationship Characteristics
	02	35	67
NOTE: Abbreviations: IQR, interquartile range; LOS, length of stay; ULOS, unnecessary length of stay. a Not significant.
LOS, d, n=293
Median	4	5	3
IQR	5	4	3
Mean	4.7 (2.72)	4.7 (2.52)	4.1 (2.51), P=0.12a
ULOS, d, n=293
Median	0	0	0
IQR	0	0	0
Mean	0.37 (0.99)	0.33 (0.96)	0.13 (0.56), P=0.09a
Complications (per patient per day), n=398
Median	0	0	0
IQR	1	1	0
Mean	0.58 (1.06)	0.45 (0.77)	0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics

Table 4.Association Between Inpatient Physician Team Relationship Characteristics and Outcomes
	Mind/Trust		Diversity/Respect		Heed/Relate/Communicate
Patient Outcome	None	Both	None	Both	None	All 3
NOTE: Abbreviations: IQR, interquartile range; LOS, length of stay; ULOS, unnecessary length of stay. a Not significant.
LOS, d, n=293
Median	4	4	4	4	4	4
IQR	5	3	4.5	3	4	4
Mean	4.7 (2.6)	4.2 (2.5)	4.7 (2.6)	4.3 (2.5)	4.4 (2.6)	4.4 (2.6)
P value	0.06a		0.23a		0.85a
ULOS, d, n=293
Median	0	0	0	0	0	0
IQR	0	0	0	0	0	0
Mean	0.39 (1.01)	0.15 (0.62)	0.33 (0.92)	0.18 (0.71)	0.32 (0.93)	0.18 (0.69)
P value	0.009		0.06		0.03
Complications (per patient), n=389
Median	0	0	0	0	0	0
IQR	1	0	1	0	1	0
Mean	0.58 (1.01)	0.19 (0.58)	0.47 (0.81)	0.29 (0.82)	0.26 (0.92)	0.28 (0.70)
P value	<0.0001		0.001		0.02

DISCUSSION

Acknowledgements

The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.

METHODS

Overview

Setting and Study Participants

Data Collection

Analysis Phase I: Assessment of Relationship Characteristics

Analysis Phase II: Factor Analysis

Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes

RESULTS

Table 1.Relationship Characteristics and Observed Behaviors
Relationship Characteristic	Definition	Thirteen Types of Behaviors Observed in Field Notes	Observed Examples
Trust	Willingness to be vulnerable to others	Use of we instead of you or I by the attending	Where are we going with this guy?
Trust	Willingness to be vulnerable to others	Attending admitting I don't know	Let's go talk to him, I can't figure this out
Asking questions to help team members to think through problems	Will the echo change our management? How will it help us?
Diversity	Including different perspectives and different thinking	Team member participation in conversations about patients that are not theirs	One intern is presenting, another intern asks a question, and the resident joins the discussion
Diversity	Including different perspectives and different thinking	Inclusion of perspectives of those outside the team (nursing and family members)	Taking a break to call the nurse, having a family meeting
Respect	Valuing the opinions of others, honest and tactful interactions	Use of positive reinforcement by the attending	Being encouraging of the medical student's differential, saying excellent
Respect		How the team talks with patients	Asking if the patient has any concerns, what they can do to make them comfortable
Heedfulness	Awareness of how each person's roles impact the rest of the team	Team members performing tasks not expected of their role	One intern helping another with changing orders to transfer a patient
Heedfulness		Summarizing plans and strategizing	Attending recaps the plan for the day, asks what they can do
Mindfulness	Openness to new ideas/free discussion about what is and is not working	Entire team engaged in discussion	Attending asks the medical student, intern, and resident what they think is going on
Social relatedness	Having socially related interactions	Social conversation among team members	Intern talks about their day off
Social relatedness	Having socially related interactions	Jokes by the attending	Showers and a bowel movement is the key to making people happy
Appropriate use of rich communication	Use of in‐person communication for sensitive or difficult issues	Using verbal communication with consultants or family	Intern is on the phone with the pharm D because there is a problem with the medication

Table 2.Team Relationship Profiles
Relationship Characteristic	Team
Relationship Characteristic	1	2	3a	3b	4a	4b	5	6	7	8	9
Trust	0	0	1	0	0	0	1	0	1	1	1
Diversity	0	1	1	1	0	0	0	0	1	1	1
Respect	0	1	1	1	0	1	0	0	1	1	1
Heedfulness	0	1	1	0	1	0	1	0	1	1	1
Mindfulness	0	0	1	0	0	1	1	0	1	1	1
Social/task relatedness	0	1	1	0	1	1	1	0	1	1	1
Rich/lean communication	0	1	1	0	0	0	1	0	1	1	0
Relationship score (no. of characteristics observed)	0	5	7	2	2	3	5	0	7	7	6

Table 3.Association Between the Teams' Number of Relationship Characteristics and Patient Outcomes
	No. of Relationship Characteristics
	02	35	67
NOTE: Abbreviations: IQR, interquartile range; LOS, length of stay; ULOS, unnecessary length of stay. a Not significant.
LOS, d, n=293
Median	4	5	3
IQR	5	4	3
Mean	4.7 (2.72)	4.7 (2.52)	4.1 (2.51), P=0.12a
ULOS, d, n=293
Median	0	0	0
IQR	0	0	0
Mean	0.37 (0.99)	0.33 (0.96)	0.13 (0.56), P=0.09a
Complications (per patient per day), n=398
Median	0	0	0
IQR	1	1	0
Mean	0.58 (1.06)	0.45 (0.77)	0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics

Table 4.Association Between Inpatient Physician Team Relationship Characteristics and Outcomes
	Mind/Trust		Diversity/Respect		Heed/Relate/Communicate
Patient Outcome	None	Both	None	Both	None	All 3
NOTE: Abbreviations: IQR, interquartile range; LOS, length of stay; ULOS, unnecessary length of stay. a Not significant.
LOS, d, n=293
Median	4	4	4	4	4	4
IQR	5	3	4.5	3	4	4
Mean	4.7 (2.6)	4.2 (2.5)	4.7 (2.6)	4.3 (2.5)	4.4 (2.6)	4.4 (2.6)
P value	0.06a		0.23a		0.85a
ULOS, d, n=293
Median	0	0	0	0	0	0
IQR	0	0	0	0	0	0
Mean	0.39 (1.01)	0.15 (0.62)	0.33 (0.92)	0.18 (0.71)	0.32 (0.93)	0.18 (0.69)
P value	0.009		0.06		0.03
Complications (per patient), n=389
Median	0	0	0	0	0	0
IQR	1	0	1	0	1	0
Mean	0.58 (1.01)	0.19 (0.58)	0.47 (0.81)	0.29 (0.82)	0.26 (0.92)	0.28 (0.70)
P value	<0.0001		0.001		0.02

DISCUSSION

Acknowledgements

The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.

References

Plsek P. Redesigning health care with insights from the science of complex adaptive systems. In: Crossing the Quality Chasm: A New Heath System for the 21st Century. Washington, DC: National Academy of Sciences; 2000:309–322.
Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;323(22):2124–2135.
Krauss MJ, Nguyen SL, Dunagan WC, et al. Circumstances of patient falls and injuries in 9 hospitals in a mid‐western healthcare system. Infect Control Hosp Epidemiol. 2007;28(5):544–550.
Hurd T, Posnett J. Point prevalence of wounds in a sample of acute hospitals in Canada. Int Wound J. 2009;6(4):287–293.
Garcin F, Leone M, Antonini F, Charvet A, Albanese J, Martin C. Non‐adherence to guidelines: an avoidable cause of failure of empirical antimicrobial therapy in the presence of difficult‐to‐treat bacteria. Intensive Care Med. 2010;36(1):75–82.
Williams SC, Schmaltz SP, Morton DJ, Koss RG, Loeb JM. Quality of care in U.S. hospitals as reflected by standardized measures, 2002–2004. N Engl J Med. 2005;353(3):255–264.
Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases. Division of Healthcare Quality Promotion. Checklist for prevention of central line associated blood stream infections. Available at: http://www.cdc.gov/HAI/pdfs/bsi/checklist‐for‐CLABSI.pdf. Accessed August 3, 2014.
Safer Healthcare Partners, LLC. Checklists: a critical patient safety tool. Available at: http://www.saferhealthcare.com/high‐reliability‐topics/checklists. Accessed July 31, 2014.
Yam Y. Making Things Work: Solving Complex Problems in a Complex World. Boston, MA: Knowledge Press; 2004:117–160.
Gittell JH. High Performance Healthcare: Using The Power of Relationships to Achieve Quality, Efficiency, and Resilience. 1st ed. New York, NY: McGraw‐Hill; 2009.
Carroll JS, Rudolph JW. Design of high reliability organizations in health care. Qual Saf Health Care. 2006;15(suppl 1):i4–i9.
Salas E, DiazGranados D, Weaver SJ, King H. Does team training work? Principles for health care. Acad Emerg Med. 2008;15(11):1002–1009.
Edmondson A. Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manag Stud. 2003;40(6):1419–1452.
Neily J, Mills PD, Young‐Xu Y, et al. Association between implementation of a medical team training program and surgical mortality. JAMA. 2010;304(15):1693–1700.
Lewis K, Belliveau M, Herndon B, Keller J. Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge. Organ Behav Hum Decis Process. 2007;103(2):159–178.
Leykum LK, Palmer RF, Lanham HJ, McDaniel RR, Noel PH, Parchman ML. Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care settings. BMC Health Serv Res. 2011;11:44.
Noel PH, Lanham HJ, Palmer RF, Leykum LK, Parchman ML. The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manage Rev. 2012;38(1):20–28.
Dixon‐Woods M, Bosk CL, Aveling EL, Goeschel CA, Pronovost PJ. Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.
Lanham HJ, McDaniel RR, Crabtree BF, et al. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457–466.
Finely EP, Pugh JA, Lanham HJ, et al. Relationship quality and patient‐assessed quality of care in VA primary care clinics: development and validation of the work relationships scale. Ann Fam Med. 2013;11(6):543–549.
Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. 2nd ed. Thousand Oaks, CA: Sage; 2011.
Patton MQ. Qualitative Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
Pope C, Royen P, Baker R. Qualitative methods in research on health care quality. Qual Saf Health Care. 2002;11:148–152.
Hoff T. Managing the negatives of experience in physician teams. Health Care Manage Rev. 2010;35(1):65–76.
Tamuz M, Giardina TD, Thomas EJ, Menon S, Singh H. Rethinking resident supervision to improve safety: from hierarchical to interprofessional models. J Hosp Med. 2011;6(8):445 b452.
Klein KJ, Ziegart JC, Knight AP, Xiao Y. Dynamic delegation: shared, hierarchical, and deindividualized leadership in extreme action teams. Adm Sci Q. 2006;51(4):590–621.
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
Tukey JW. Exploratory Data Analysis. Reading, MA: Addison‐Wesley; 1977.
Zar JH. Biostatistical Analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice‐Hall; 2010.
Dunn OJ. Multiple contrasts using rank sums. Technometrics. 1964;6:241–252.
Elliott AC, Hynan LS. A SAS macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Comput Methods Programs Biomed. 2011;102:75–80.
SAS/STAT Software [computer program]. Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029–1034.
Nonaka I. A dynamic theory of organizational knowledge creation. Org Sci. 1994;5(1):14–37.
Edmundson AC. Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. 1st ed. Boston, MA: Harvard Business School; 2012.

References

Plsek P. Redesigning health care with insights from the science of complex adaptive systems. In: Crossing the Quality Chasm: A New Heath System for the 21st Century. Washington, DC: National Academy of Sciences; 2000:309–322.
Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ. Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;323(22):2124–2135.
Krauss MJ, Nguyen SL, Dunagan WC, et al. Circumstances of patient falls and injuries in 9 hospitals in a mid‐western healthcare system. Infect Control Hosp Epidemiol. 2007;28(5):544–550.
Hurd T, Posnett J. Point prevalence of wounds in a sample of acute hospitals in Canada. Int Wound J. 2009;6(4):287–293.
Garcin F, Leone M, Antonini F, Charvet A, Albanese J, Martin C. Non‐adherence to guidelines: an avoidable cause of failure of empirical antimicrobial therapy in the presence of difficult‐to‐treat bacteria. Intensive Care Med. 2010;36(1):75–82.
Williams SC, Schmaltz SP, Morton DJ, Koss RG, Loeb JM. Quality of care in U.S. hospitals as reflected by standardized measures, 2002–2004. N Engl J Med. 2005;353(3):255–264.
Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases. Division of Healthcare Quality Promotion. Checklist for prevention of central line associated blood stream infections. Available at: http://www.cdc.gov/HAI/pdfs/bsi/checklist‐for‐CLABSI.pdf. Accessed August 3, 2014.
Safer Healthcare Partners, LLC. Checklists: a critical patient safety tool. Available at: http://www.saferhealthcare.com/high‐reliability‐topics/checklists. Accessed July 31, 2014.
Yam Y. Making Things Work: Solving Complex Problems in a Complex World. Boston, MA: Knowledge Press; 2004:117–160.
Gittell JH. High Performance Healthcare: Using The Power of Relationships to Achieve Quality, Efficiency, and Resilience. 1st ed. New York, NY: McGraw‐Hill; 2009.
Carroll JS, Rudolph JW. Design of high reliability organizations in health care. Qual Saf Health Care. 2006;15(suppl 1):i4–i9.
Salas E, DiazGranados D, Weaver SJ, King H. Does team training work? Principles for health care. Acad Emerg Med. 2008;15(11):1002–1009.
Edmondson A. Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manag Stud. 2003;40(6):1419–1452.
Neily J, Mills PD, Young‐Xu Y, et al. Association between implementation of a medical team training program and surgical mortality. JAMA. 2010;304(15):1693–1700.
Lewis K, Belliveau M, Herndon B, Keller J. Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge. Organ Behav Hum Decis Process. 2007;103(2):159–178.
Leykum LK, Palmer RF, Lanham HJ, McDaniel RR, Noel PH, Parchman ML. Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care settings. BMC Health Serv Res. 2011;11:44.
Noel PH, Lanham HJ, Palmer RF, Leykum LK, Parchman ML. The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manage Rev. 2012;38(1):20–28.
Dixon‐Woods M, Bosk CL, Aveling EL, Goeschel CA, Pronovost PJ. Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.
Lanham HJ, McDaniel RR, Crabtree BF, et al. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457–466.
Finely EP, Pugh JA, Lanham HJ, et al. Relationship quality and patient‐assessed quality of care in VA primary care clinics: development and validation of the work relationships scale. Ann Fam Med. 2013;11(6):543–549.
Creswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. 2nd ed. Thousand Oaks, CA: Sage; 2011.
Patton MQ. Qualitative Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
Pope C, Royen P, Baker R. Qualitative methods in research on health care quality. Qual Saf Health Care. 2002;11:148–152.
Hoff T. Managing the negatives of experience in physician teams. Health Care Manage Rev. 2010;35(1):65–76.
Tamuz M, Giardina TD, Thomas EJ, Menon S, Singh H. Rethinking resident supervision to improve safety: from hierarchical to interprofessional models. J Hosp Med. 2011;6(8):445 b452.
Klein KJ, Ziegart JC, Knight AP, Xiao Y. Dynamic delegation: shared, hierarchical, and deindividualized leadership in extreme action teams. Adm Sci Q. 2006;51(4):590–621.
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
Tukey JW. Exploratory Data Analysis. Reading, MA: Addison‐Wesley; 1977.
Zar JH. Biostatistical Analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice‐Hall; 2010.
Dunn OJ. Multiple contrasts using rank sums. Technometrics. 1964;6:241–252.
Elliott AC, Hynan LS. A SAS macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Comput Methods Programs Biomed. 2011;102:75–80.
SAS/STAT Software [computer program]. Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
Ghaferi AA, Birkmeyer JD, Dimick JB. Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029–1034.
Nonaka I. A dynamic theory of organizational knowledge creation. Org Sci. 1994;5(1):14–37.
Edmundson AC. Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. 1st ed. Boston, MA: Harvard Business School; 2012.

Issue

Journal of Hospital Medicine - 9(12)

Issue

Journal of Hospital Medicine - 9(12)

Page Number

764-771

Page Number

764-771

Publications

Publications

Article Type

Display Headline

Relationships within inpatient physician housestaff teams and their association with hospitalized patient outcomes

Display Headline

Relationships within inpatient physician housestaff teams and their association with hospitalized patient outcomes

Sections

Article Source

Disallow All Ads

Corresponding Author

Correspondence Location

Address for correspondence and reprint requests: Luci Leykum, MD, 7400 Merton Minter, San Antonio, TX 78229; Telephone: 210‐567‐4462; Fax: 210‐567‐0218; E‐mail: leykum@uthscsa.edu

Content Gating

No Gating (article Unlocked/Free)

Alternative CME

Article PDF Media

Media Files