Reports From the Field

Improving Hospital Metrics Through the Implementation of a Comorbidity Capture Tool and Other Quality Initiatives


 

References

Assessment of Quality Initiatives’ Impact

Data on the number of comorbidities and performance indicators were obtained retrospectively. The data included all hospital admissions from 2019 and 2020 divided into 2 periods: pre-intervention from January 1, 2019 through September 30, 2019, and intervention from October 1, 2019 through December 31, 2020. The primary outcome of this observational study was the rate of comorbidity capture during the intervention period. Comorbidity capture was assessed using the Vizient Clinical Data Base (CDB) health care performance tool.5 Vizient CDB uses the Agency for Healthcare Research and Quality Elixhauser index, which includes 29 of the initial 31 comorbidities described by Elixhauser,6 as it combines hypertension with and without complications into one. We secondarily aimed to examine the impact of the quality improvement initiatives on several institutional-level performance indicators, including total number of diagnoses, comorbidities or complications (CC), major comorbidities or complications (MCC), CMI, and expected mortality.

Case mix index is the average Medicare Severity-DRG (MS-DRG) weighted across all hospital discharges (appropriate to their discharge date). The expected mortality represents the average expected number of deaths based on diagnosed conditions, age, and gender within the same time frame, and it is based on coded diagnosis; we obtained the mortality index by dividing the observed mortality by the expected mortality. The Vizient CDB Mortality Risk Adjustment Model was used to assign an expected mortality (0%-100%) to each case based on factors such as demographics, admission type, diagnoses, and procedures.

Standard statistics were used to measure the outcomes. We used Excel to compare pre-intervention and intervention period characteristics and outcomes, using t-testing for continuous variables and Chi-square testing for categorial outcomes. P values <0.05 were considered statistically significant.

The study was reviewed by the institutional review board (IRB) of our institution (IRB ID: 20210070). The IRB determined that the proposed activity was not research involving human subjects, as defined by the Department of Health and Human Services and US Food and Drug Administration regulations, and that IRB review and approval by the organization were not required.

Results

The health system had a total of 33 066 admissions during the study period—13 689 pre-intervention (January 1, 2019 through September 30, 2019) and 19,377 during the intervention period (October 1, 2019 to December 31, 2020). Demographics were similar among the pre-intervention and intervention periods: mean age was 60 years and 61 years, 52% and 51% of patients were male, 72% and 71% were White, and 20% and 19% were Black, respectively (Table 1).

tables and figures for JCOM

The multifaceted intervention resulted in a significant improvement in the primary outcome: mean comorbidity capture increased from 2.5 (SD, 1.7) before the intervention to 3.1 (SD, 2.0) during the intervention (P < .00001). Secondary outcomes also improved. The mean number of secondary diagnoses for admissions increased from 11.3 (SD, 7.3) prior to the intervention to 18.5 (SD, 10.4) (P < .00001) during the intervention period. The mean CMI increased from 2.1 (SD, 1.9) to 2.4 (SD, 2.2) post intervention (P < .00001), an increase during the intervention period of 14%. The expected mortality increased from 1.8% (SD, 6.1%) to 3.1% (SD, 9.2%) after the intervention (P < .00001) (Table 2).

tables and figures for JCOM

There was an overall observed improvement in percentage of discharges with documented CC and MCC for both surgical and medical specialties. Both CC and MCC increased for surgical specialties, from 54.4% to 68.5%, and for medical specialties, from 68.9% to 76.4%. (Figure 1). The diagnoses that were captured more consistently included deficiency anemia, obesity, diabetes with complications, fluid and electrolyte disorders and renal failure, hypertension, weight loss, depression, and hypothyroidism (Figure 2). A summary of the timeline of interventions overlaid with CMI and expected mortality is shown in Figure 3.

tables and figures for JCOM
tables and figures for JCOM
tables and figures for JCOM

During the 9-month pre-intervention period (January 1 through September 30, 2019), there were 2795 queries, with an agreed volume of 1823; the agreement rate was 65% and the average provider turnaround time was 12.53 days. In the 15-month postintervention period, there were 10 216 queries, with an agreed volume of 6802 at 66%. We created a policy to encourage responses no later than 10 days after the query, and our average turnaround time decreased by more than 50% to 5.86 days. The average number of monthly queries increased by 55%, from an average of 311 monthly queries in the pre-intervention period to an average of 681 per month in the postintervention period. The more common queries that had an impact on CMI included sepsis, antineoplastic chemotherapy–induced pancytopenia, acute posthemorrhagic anemia, malnutrition, hyponatremia, and metabolic encephalopathy.

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