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Study Overview
Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.
Design. Cross-sectional survey-based study.
Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.
Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).
The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.
Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).
More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.
58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).
Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.
Commentary
Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology to improve population health outcomes and health care quality, and to achieve health equity” [8].
However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.
A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.
The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.
Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].
Applications For Clinical Practice
There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.
—Katrina F. Mateo, MPH
1. Nutbeam D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promot Int 2000;15:259–67.
2. Greene J, Hibbard JH. Why does patient activation matter? an examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med 2011;27:520–6.
3. Coulter A. Patient engagement--what works? J Ambul Care Manage 2012;35:80–9.
4. Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. Health Aff (Millwood) 2013;32:207–14.
5. Hibbard JH, Greene J, Overton V. Patients with lower activation associated with higher costs; delivery systems should know their patients’ “scores”. Health Aff (Millwood) 2013;32:216–22.
6. Nelson DE, Kreps GL, Hesse BW, et al. The Health Information National Trends Survey (HINTS): development, design, and dissemination. J Health Commun 2004;9:443–60.
7. Truog RD. Patients and doctors--evolution of a relationship. N Engl J Med 2012;366:581–5.
8. Office of Disease Prevention and Health Promotion. Health Communication and Health Information Technology. Available at www.healthypeople.gov/2020/topics-objectives/topic/health-communication-and-health-information-technology.
9. Fox S. Social media in context. Pew Research Center. 2011. Available at www.pewinternet.org/2011/05/12/social-media-in-context/.
10. Christmas C, Durso SC, Kravet SJ, Wright SM. Advantages and challenges of working as a clinician in an academic department of medicine: academic clinicians’ perspectives. J Grad Med Educ 2010;2:478–84.
11. Kane NM, Singer SJ, Clark JR, et al. Strained local and state government finances among current realities that threaten public hospitals’ profitability. Health Aff (Millwood) 2012;31:1680–9.
12. Felland LE, Stark L. Local public hospitals: changing with the times. Res Brief 2012;(25):1–13.
13. Schwartzberg JG, Cowett A, VanGeest J, Wolf MS. Communication techniques for patients with low health literacy: a survey of physicians, nurses, and pharmacists. Am J Health Behav 2007;31 Suppl 1:S96–104.
14. Stocks NP, Hill CL, Gravier S, et al. Health literacy--a new concept for general practice? Aust Fam Physician 2009;38:144–7.
15. Warren J, Kvasny L, Hecht M, et al. Barriers, control and identity in health information seeking among African American women. J Health Dispar Res Pract 2012;3(3).
16. Wolf MS, Williams MV, Parker RM, et al. Patients’ shame and attitudes toward discussing the results of literacy screening. J Health Commun 2007;12:721–32.
Study Overview
Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.
Design. Cross-sectional survey-based study.
Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.
Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).
The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.
Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).
More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.
58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).
Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.
Commentary
Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology to improve population health outcomes and health care quality, and to achieve health equity” [8].
However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.
A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.
The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.
Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].
Applications For Clinical Practice
There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.
—Katrina F. Mateo, MPH
Study Overview
Objective. To assess how patients look for patient-obtained medication information (POMI) to prepare for a clinical appointment, whether they share those findings with their provider, and how health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI.
Design. Cross-sectional survey-based study.
Setting and participants. The study took place over 1 week at 2 academic medical centers located in Las Vegas, Nevada, and Washington, DC. At a central waiting area at each facility, patients aged 18 and older waiting for their clinical appointment were invited to complete a survey, either on a computer tablet or with paper and pencil, before and after their appointment.
Measures and analysis. The pre-survey included demographic measures (age, gender, education, and ethnicity), the reason for the visit (routine care, sick visit, follow-up after survey, and follow-up after emergency room visit), and an item to assess self-report of perceived general health (from poor to excellent). Health literacy was assessed by a self-report measure that included subscales for the 3 dimensions of health literacy: functional, communicative, and critical health literacy [1]; together, these capture the ability of patients to retain health knowledge, gather and communicate health concepts, and apply health information. Patient activation was scored using the Patient Activation Measure (13 Likert-style items, total scale range 0–100); patient activation combines a patient’s self-reported knowledge, skill, and confidence for self-management of general health or a chronic condition [2]. Information seeking was measured by time spent (did not look for information, 1 hour, 2 hours, 3 hours, or more than 3 hours), and information channels used to look for POMI (eg, magazines/newspapers, internet website or search engine) were presented dichotomously (yes/no).
The post-survey first asked whether the participant shared information with their provider (yes/no). If the participant said yes, 4 items assessed their perception of the provider’s response, including amount of time spent discussing POMI, how seriously the provider considered the information, and overall reaction (scored as a mean, each item measured from 1–5, with 5 indicating the most positive reactions). For hypothesis testing, logistic regression models were used to test the effects of the independent variables. To explore the relationship between health literacy/patient activation and physician response, correlations were calculated.
Main results. Over 400 patients were asked to participate, and of these a total of 243 (60.75%) patients were eligible, consented, and completed surveys. Participants were predominantly white (57.6%), female (63%), had some college education or higher (80.2%), and had a clinical appointment for routine care (69.3%). The mean age was 47.04 years (SD, 15.78), the mean health status was 3.20 (SD, 0.94), and the mean Patient Activation Measure was 72.43 (SD, 16.00).
More than half of participants (58.26%) who responded to the item about information seeking indicated seeking POMI prior to their clinical appointment. Of these, the majority (88.7%) reported using the internet, particularly WebMD, as an information channel. Significant predictors of information seeking included age (P = 0.01, OR = 0.973), communicative health literacy (P = 0.01, or = 1.975), and critical health literacy (P = 0.05, OR = 1.518). Lower age, higher communicative health literacy, and higher critical health literacy increased the likelihood of the patient seeking POMI prior to the clinical appointment. Other assessed predictors were not significant, including gender, functional health literacy, patient activation, reason for visit, and reported health status.
58.2% of the 141 information-seeking patients talked to their health care provider about the information they found. However, no predictor variables included in a logistic regression analysis were significant, including age, gender, reason for visit, reported health status, functional health literacy, communicative health literacy, critical health literacy, and patient activation. For the research question (how do health literacy and patient activation relate to a patient’s perception of the physician’s reaction to POMI), the mean score on the 4-item measure was 4.08 (SD, 0.90), indicating a generally positive response; most reported the physician response was good or higher. Patient activation correlated positively with perceived physician response (r = 0.245, P = 0.03).
Conclusion. The lack of data to predict who will introduce POMI at the medical visit is disconcerting. Providers might consider directly asking or passively surveying what outside information sources the patient has engaged with, regardless of whether patient introduces the information or does not introduce it.
Commentary
Patient engagement plays an important role in health care [3]. Activated patients often have skills and confidence to engage in their health care and with their provider, which often contributes to better health outcomes and care experiences [2,4] as well as lower health care costs [5]. Health information is needed to make informed decisions, manage health, and practice healthy behaviors [6], and patients are increasingly taking an active role in seeking out medical or health information outside of the clinical encounter in order to make shared health decisions with their provider [7]. Indeed, one of the Healthy People 2020 goals is to “Use health communication strategies and health information technology to improve population health outcomes and health care quality, and to achieve health equity” [8].
However, seeking POMI requires health literacy skills and supportive relationships, particularly when navigating the many channels and complexities of publicly available health information [8]. This is especially true on the internet, where there is often varying accuracy and clarity of information presented. According to 2011 data from the Pew Research Center [9], 74% of adults in the United States use the internet, and of those adults 80% have looked online for health information; 34% have read another person’s commentary or experience about health or medical issues on an online news group, website, or blog; 25% have watched an online video about health or medical issues; and 24% have consulted online reviews of particular drugs or medical treatments.
A general strength of this study was the cross-sectional design, which allowed for surveying patients around attitudes, motivations, and behaviors immediately before and after their clinical encounter. According to the authors, this study design was aimed to extend knowledge around information seeking and provider discussions that have occurred distally and relied on patient long-term recall. Additionally, this study surveyed a variety of patients (not limited to either primary or specialist appointments) at 2 different academic medical centers, and gave patients a choice to either take the survey on a computer tablet or traditional paper and pencil. Further, the authors assessed the reliability of scales used and included a number of predictor variables in the logistic regression models for hypothesis testing.
The authors acknowledged several limitations, including the use of convenience sampling and self-reported data with volunteer participants, which can result in self-selection bias and social desirability bias. As study participants were self-selecting, low health literacy patients may have been more likely to not volunteer to take the survey, which might explain the relatively high mean scores on the health literacy measures. Further, participants were mostly white, female, college-educated, health literate, and scheduled for a routine visit, which limits the generalizability of the study findings and the ability to identify significant predictors.
Regarding the study design, pre-/post-tests are usually used to measure the change in a situation, phenomenon, problem, or attitude. However, as the authors did not aim to measure any change during the clinical encounter itself, the use of only a post-test may have been more appropriate. The use of a pre-/post-test design may have increased the likelihood of patients both recalling POMI before the encounter and then sharing POMI with their provider. Also, in the post-survey, the authors only asked follow-up questions of patients that shared POMI with their provider. An open-response question could have been included to explore further why some patients chose not to introduce POMI during the clinical encounter. Lastly, the authors may have been able to reach more patients with lower health literacy if surveys were administered at public hospitals as opposed to academic medical centers. While some providers may perceive that patients in academic medical centers are more complex or may have limited access to care [10], patients at public hospitals and safety net hospitals tend to be of lower income and have limited or no insurance [11,12].
Applications For Clinical Practice
There are documented communication-enhancing techniques and strategies that providers and other health professionals use, particularly among patients with low health literacy [13]. Based on this study, the authors conclude that providers may try another strategy of directly asking or passively surveying any POMI, regardless of whether the patient initiates this conversation. Other research has acknowledged that recognition of health literacy status allows for the use of appropriate communication tools [14]. However, providers need to recognize barriers to health information seeking, particularly among minorities and underserved populations [15], as well as the potential for embarrassment that patients might experience as a result of revealing misunderstandings of health information or general reading difficulties [16]. This study highlights the need for further research to identify predictors of health information seeking and especially health information sharing by patients during the clinical encounter.
—Katrina F. Mateo, MPH
1. Nutbeam D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promot Int 2000;15:259–67.
2. Greene J, Hibbard JH. Why does patient activation matter? an examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med 2011;27:520–6.
3. Coulter A. Patient engagement--what works? J Ambul Care Manage 2012;35:80–9.
4. Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. Health Aff (Millwood) 2013;32:207–14.
5. Hibbard JH, Greene J, Overton V. Patients with lower activation associated with higher costs; delivery systems should know their patients’ “scores”. Health Aff (Millwood) 2013;32:216–22.
6. Nelson DE, Kreps GL, Hesse BW, et al. The Health Information National Trends Survey (HINTS): development, design, and dissemination. J Health Commun 2004;9:443–60.
7. Truog RD. Patients and doctors--evolution of a relationship. N Engl J Med 2012;366:581–5.
8. Office of Disease Prevention and Health Promotion. Health Communication and Health Information Technology. Available at www.healthypeople.gov/2020/topics-objectives/topic/health-communication-and-health-information-technology.
9. Fox S. Social media in context. Pew Research Center. 2011. Available at www.pewinternet.org/2011/05/12/social-media-in-context/.
10. Christmas C, Durso SC, Kravet SJ, Wright SM. Advantages and challenges of working as a clinician in an academic department of medicine: academic clinicians’ perspectives. J Grad Med Educ 2010;2:478–84.
11. Kane NM, Singer SJ, Clark JR, et al. Strained local and state government finances among current realities that threaten public hospitals’ profitability. Health Aff (Millwood) 2012;31:1680–9.
12. Felland LE, Stark L. Local public hospitals: changing with the times. Res Brief 2012;(25):1–13.
13. Schwartzberg JG, Cowett A, VanGeest J, Wolf MS. Communication techniques for patients with low health literacy: a survey of physicians, nurses, and pharmacists. Am J Health Behav 2007;31 Suppl 1:S96–104.
14. Stocks NP, Hill CL, Gravier S, et al. Health literacy--a new concept for general practice? Aust Fam Physician 2009;38:144–7.
15. Warren J, Kvasny L, Hecht M, et al. Barriers, control and identity in health information seeking among African American women. J Health Dispar Res Pract 2012;3(3).
16. Wolf MS, Williams MV, Parker RM, et al. Patients’ shame and attitudes toward discussing the results of literacy screening. J Health Commun 2007;12:721–32.
1. Nutbeam D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promot Int 2000;15:259–67.
2. Greene J, Hibbard JH. Why does patient activation matter? an examination of the relationships between patient activation and health-related outcomes. J Gen Intern Med 2011;27:520–6.
3. Coulter A. Patient engagement--what works? J Ambul Care Manage 2012;35:80–9.
4. Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. Health Aff (Millwood) 2013;32:207–14.
5. Hibbard JH, Greene J, Overton V. Patients with lower activation associated with higher costs; delivery systems should know their patients’ “scores”. Health Aff (Millwood) 2013;32:216–22.
6. Nelson DE, Kreps GL, Hesse BW, et al. The Health Information National Trends Survey (HINTS): development, design, and dissemination. J Health Commun 2004;9:443–60.
7. Truog RD. Patients and doctors--evolution of a relationship. N Engl J Med 2012;366:581–5.
8. Office of Disease Prevention and Health Promotion. Health Communication and Health Information Technology. Available at www.healthypeople.gov/2020/topics-objectives/topic/health-communication-and-health-information-technology.
9. Fox S. Social media in context. Pew Research Center. 2011. Available at www.pewinternet.org/2011/05/12/social-media-in-context/.
10. Christmas C, Durso SC, Kravet SJ, Wright SM. Advantages and challenges of working as a clinician in an academic department of medicine: academic clinicians’ perspectives. J Grad Med Educ 2010;2:478–84.
11. Kane NM, Singer SJ, Clark JR, et al. Strained local and state government finances among current realities that threaten public hospitals’ profitability. Health Aff (Millwood) 2012;31:1680–9.
12. Felland LE, Stark L. Local public hospitals: changing with the times. Res Brief 2012;(25):1–13.
13. Schwartzberg JG, Cowett A, VanGeest J, Wolf MS. Communication techniques for patients with low health literacy: a survey of physicians, nurses, and pharmacists. Am J Health Behav 2007;31 Suppl 1:S96–104.
14. Stocks NP, Hill CL, Gravier S, et al. Health literacy--a new concept for general practice? Aust Fam Physician 2009;38:144–7.
15. Warren J, Kvasny L, Hecht M, et al. Barriers, control and identity in health information seeking among African American women. J Health Dispar Res Pract 2012;3(3).
16. Wolf MS, Williams MV, Parker RM, et al. Patients’ shame and attitudes toward discussing the results of literacy screening. J Health Commun 2007;12:721–32.