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The American Journal of Managed Care October 2017
Low-Value Antibiotic Prescribing and Clinical Factors Influencing Patient Satisfaction
Adam L. Sharp, MD, MS; Ernest Shen, PhD; Michael H. Kanter, MD; Laura J. Berman, MPH; and Michael K. Gould, MD, MS
Countywide Physician Organization Learning Collaborative and Changes in Hospitalization Rates
Brent D. Fulton, PhD, MBA; Susan L. Ivey, MD, MHSA; Hector P. Rodriguez, PhD, MPH; and Stephen M. Shortell, PhD, MPH, MBA
Boosting Workplace Wellness Programs With Financial Incentives
Alison Cuellar, PhD; Amelia M. Haviland, PhD; Seth Richards-Shubik, PhD; Anthony T. LoSasso, PhD; Alicia Atwood, MPH; Hilary Wolfendale, MA; Mona Shah, MS; and Kevin G. Volpp, MD, PhD
Use of Patient-Reported Outcomes and Satisfaction for Quality Assessments
Anne P. Ehlers, MD, MPH; Sara Khor, MS; Amy M. Cizik, PhD, MPH; Jean-Christophe A. Leveque, MD; Neal S. Shonnard, MD; Rod J. Oskouian, Jr, MD; David R. Flum, MD, MPH; and Danielle C. Lavallee, PharmD,
Trends in Hospital–Physician Integration in Medical Oncology
Jeffrey D. Clough, MD, MBA; Michaela A. Dinan, PhD; and Kevin A. Schulman, MD
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Julia Thornton Snider, PhD; Katharine Batt, MD, MSc; Yanyu Wu, PhD; Mahlet Gizaw Tebeka, MS; and Seth Seabury, PhD
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Statewide Data Infrastructure Supports Population Health Management: Diabetes Case Study
Craig Jones, MD; Mary Kate Mohlman, PhD; David Jorgenson, MS; Karl Finison, MA; Katie McGee, MS; and Hans Kastensmith
A Health Plan's Investigation of Healthy Days and Chronic Conditions
Tristan Cordier, MPH; S. Lane Slabaugh, PharmD; Eric Havens, MA; Jonathan Pena, MS; Gil Haugh, MS; Vipin Gopal, PhD; Andrew Renda, MD; Mona Shah, PhD; and Matthew Zack, MD

Statewide Data Infrastructure Supports Population Health Management: Diabetes Case Study

Craig Jones, MD; Mary Kate Mohlman, PhD; David Jorgenson, MS; Karl Finison, MA; Katie McGee, MS; and Hans Kastensmith
The use of statewide data infrastructure is effective at identifying criteria for diabetes outreach and management at the whole-population level.
Between January 1, 2014, and December 31, 2014, claims data identified 283,153 individuals attributed to patient-centered medical homes in Vermont. Of these, 19,000 (6.7%) were categorized as having diabetes. Of this population, 6719 (35.4%) had clinical data from the same time period that could be linked to claims data. Table 1 shows the demographic characteristics of the patients with diabetes with linked data and those without linked data. The 6719 individuals with clinical data were similar to the unlinked population according to proportions seen in demographic characteristics and 3M clinical risk groups (CRGs) (Table 1). Although P values indicate statistically significant differences, this finding is likely due to large sample sizes that can exaggerate minor differences. 

During this 1-year period, per capita total healthcare expenditures for patients with diabetes varied substantially but had little correlation with control of diabetes as measured by the most recent A1C result (“not insulin dependent” R2 value = <0.001; “insulin dependent” R2 value = 0.01) (Figure 1). Overall, total medical expenditures averaged $14,948 per patient, with an average of $16,644 for patients with A1C ≤6%; $14,230 for patients with A1C >6% and ≤9%; and an average of $16,484 for patients with A1C >9%. Notably, 23% of the population with A1C >6% and ≤9% had expenditures in the highest quartile, whereas 19% of the population with A1C >9% had expenditures in the lowest quartile. There was similar variation for those on insulin therapy: 25% of patients with A1C >6% and ≤9% had expenditures in the highest quartile of this subpopulation and 32% with A1C >9% had expenditures in the lowest quartile. 

Diabetes and Comorbidities

With the poor correlation between same-year expenditures and A1C results, common comorbid conditions and clinical risk factors (ie, blood pressure, BMI, A1C, insulin dependence, asthma, COPD, CHF, CHD, renal failure, and depression) became the next focus of analysis. The relative influence on per capita annual expenditures and inpatient hospital admissions is shown for patients with diabetes with each characteristic, controlling for the other factors (Figures 2 and 3). Comorbidities that had the largest relative impact on expenditures included, in descending order, renal failure, CHF, insulin dependence, COPD, and discordant blood pressure. Comorbidities that had the largest relative impact on inpatient hospital admissions were, in descending order, CHF, renal failure, discordant blood pressure, and COPD. 

Analysis showed that the total financial impact each characteristic had depended on the size of the cohort with that characteristic. For example, A1C >9% was associated with a 16% relative increase in annual expenditures per patient, which, when aggregated across the 726 patients identified as having poor control of A1C, resulted in a total cost of $1,560,931. By comparison, CHF was associated with fewer patients (79) but with a 144% relative increase in per patient expenditures, yielding a growth in aggregate expenditures of $2,102,368.

Inpatient hospital admissions were the largest contributor to total annual expenditures, and each of the subpopulations identified by the selection criteria had hospital admissions due to ACSCs. During the 12-month study period, the population with diabetes accounted for 1384 hospital admissions, with 341 (24.6%) due to ACSCs. Based on outcomes from PQI measures, each subpopulation had hospital admissions for ACSCs that were related to the selection criteria, as well as for ACSCs that were not directly related to the selection criteria (data not shown). For example, the 233 patients identified as having diabetes and COPD had a total of 118 hospital admissions, with 56 (47.4%) due to ACSCs, 34 (60.7%) of which were due to respiratory complications. In another example, the 726 patients identified as having diabetes and A1C >9% had a total of 187 hospital admissions, with 68 (36.3%) due to ACSCs, 38 (55.9%) of which were recorded as directly related to poorly controlled diabetes. 

Table 2 provides details on overall hospital admissions, admissions due to ACSCs, admission expenditures, and estimated savings if ACSC hospital admissions were reduced by 20% and 50%. For several subpopulations, the proportion of hospital admissions due to ACSCs was higher than the overall average of 25%, including 47% of 118 admissions for patients with COPD, 42% of 81 admissions for patients with CHF, 36% of 187 admissions for patients with A1C >9%, 31% of 206 admissions for patients with blood pressure <90/60 mm Hg, and 29% of 189 admissions for patients with renal failure.

DISCUSSION

The results of this study demonstrate the power of combining traditionally separate statewide clinical and multipayer claims data. Specifically, they demonstrate how this combined dataset can be used to meet the study’s objectives, namely identifying subpopulations for outreach through practical selection criteria, determining the relative contribution of known risk factors to actual expenditures, and setting attainable care goals to reduce preventable admissions.26 Although individual organizations may have analytic capabilities to support similar modeling for their own populations, a statewide multipayer data infrastructure allows for the identification of health patterns with significant associations not readily apparent in smaller populations. This type of information becomes particularly important as Vermont’s independent practices, hospitals, and health centers work toward an accountable care framework with shared interests to control costs and improve quality.1,27 In this context, the study was designed to generate results that support proactive rather than reactive care by the patient-centered medical homes, specialists, and other community providers that are increasingly working together to improve coordination, quality, and population health outcomes.20

Diabetes provided a useful test case of a complex condition that impacts 6.7% of the medical home population aged between 18 and 75 years in Vermont. Furthermore, it has a disease course modifiable through guideline-based management that addresses risk factors such as glycemic control, blood pressure, lipid control levels, diet, and obesity.28 Although A1C level is an indicator of sustained glycemic control and long-term complications and has a positive association with health costs over time,14,29-36 this study suggests a more complex relationship between glycemic control and total healthcare expenditures in the same year.37 Figure 1 shows one-fifth of patients with poorly controlled diabetes had total healthcare costs in the lowest quartile, whereas almost a quarter of those with an A1C level in the recommended range had annual healthcare expenditures in the top quartile. Although long-term glycemic control can reduce complications and potentially avoid some healthcare costs,15,37 this study highlights the opportunity to identify care management targets to improve health outcomes and control costs in the near term. 

The selection criteria examined for their relative cost impact offer several advantages for identifying target subpopulations. First, they are commonly tracked data elements readily available to identify patient panels in most care settings with an EHR system. Second, outcomes for these comorbid conditions can be improved through recommended treatments and better disease control. For example, improved glycemic, blood pressure, and weight control can lower rates of cardiovascular, renal, and lower limb complications for patients with diabetes.28 Similarly, guideline-based management for asthma, COPD, CHF, and depression has been shown to improve health outcomes and reduce morbidity, including rates of hospital-based care.38-40 Third, diagnosis coding indicates that the subpopulations identified by the selection criteria had potentially avoidable hospital admissions both accounted and not accounted for by the ACSC measure. This finding suggests that identifying this subpopulation presents an expanded opportunity for further reducing admissions and expenditures by improving disease management and preventive care. 

An important next step is to prospectively test the use of the selection criteria to guide outreach, care management, and coordination and to determine whether the near-term utilization and financial goals can be achieved in 1 to 2 years for a community or regional population. 

Limitations

This study had several limitations. First, clinical data was available for only 6719 (35%) of the 19,000 patients with diabetes and claims data in the APCD. More complete data may or may not alter the findings, although the likelihood that the findings would change is lessened given the demographic and health status similarities between the groups with clinical data and those without. This conclusion can be confirmed as the state continually and systematically improves the scale and quality of available clinical data. Second, the selection criteria for identifying subpopulations were based on their relative association with increased expenditures and utilization. Although this is valuable for targeting utilization and cost drivers, this approach may not be the best for improving long-term health and wellness. However, these selection criteria can guide preventive care with the potential for near- and long-term positive impacts.

CONCLUSIONS

The findings in this study showed how Vermont’s policy decisions are translating into a data utility that supports a high-performing health system. In addition to its efforts of statewide multipayer delivery system reform through the Vermont Blueprint for Health program,20 the state’s commitments to developing and maintaining an APCD and an HIEN have established the data infrastructure to support the work of primary care medical homes and community health teams while improving coaching and transformation support in each service area.10 A culture of data use and shared learning continues to emerge across the state as comparative performance profiles guide ongoing improvement activities. In effect, the state’s policy decisions are steadily leading toward a data utility that can be used to drive better care and lower costs for all its residents. Infrastructure of this scale and scope would likely not be developed without sustained public commitment. 

Acknowledgments

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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