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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, PhD
Trends in Hospital–Physician Integration in Medical Oncology
Jeffrey D. Clough, MD, MBA; Michaela A. Dinan, PhD; and Kevin A. Schulman, MD
Improving Care Transitions: Complex High-Utilizing Patient Experiences Guide Reform
Nancy Ambrose Gallagher, PhD, APRN-BC; Donna Fox, RN; Carrie Dawson, MS, RN; and Brent C. Williams, MD, MPH
The Option Value of Innovative Treatments for Non–Small Cell Lung Cancer and Renal Cell Carcinoma
Julia Thornton Snider, PhD; Katharine Batt, MD, MSc; Yanyu Wu, PhD; Mahlet Gizaw Tebeka, MS; and Seth Seabury, PhD
Currently Reading
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.

Objectives: To understand how a statewide data infrastructure, including clinical and multipayer claims data, can inform preventive care and reduce medical expenditures for patients with diabetes. 

Study Design: A retrospective 1-year cross-sectional analysis of claims linked to clinical data for 6719 patients with diabetes in 2014 to evaluate impacts of comorbidities on the total cost of care. 

Methods: Initially, variation in healthcare expenditures was examined versus a measure of disease control (most recent glycated hemoglobin [A1C] test results). Multivariable linear regression calculated the relative impact of a series of risk factors on medical expenditures. Poisson regression estimated the relative impact on inpatient hospital admissions. Possible savings were estimated with a reduction in potentially avoidable hospital admissions. 

Results: No linear relationship was found between A1C and same-year medical expenditures. Comorbidities in the population with diabetes with the largest relative impact on expenditures and inpatient hospital admissions were renal failure, congestive heart failure, chronic obstructive pulmonary disease, and discordant blood pressure. Diabetes plus congestive heart failure had the highest cost per inpatient admission; diabetes plus body mass index (BMI) ≥35 had the highest aggregate costs and potential savings. 

Conclusions: A statewide data infrastructure can be used to identify criteria for outreach and population management of diabetes. The selection criteria are applicable across a statewide population and are associated with a higher relative impact on near-term expenditures than recent A1C test results. Whole-population data aggregation can be used to develop actionable information that is particularly relevant as independent organizations work together under alternative payment model arrangements.

Am J Manag Care. 2017;23(10):e331-e339
Takeaway Points

Use of statewide data infrastructure is effective at identifying criteria for diabetes outreach, management, and cost containment for a statewide population across diverse clinical settings, payers, and communities. 
  • The comorbidities identified within the population with diabetes were associated with a higher relative impact on near-term expenditures than recent glycated hemoglobin test results. 
  • Data aggregation can be used to develop actionable information for panel management across different clinical settings. 
  • Whole-population data can drive near-term cost containment through improving primary and preventive care, which is particularly relevant under alternative payment models.
States and healthcare organizations across the country are testing strategies intended to control the growth of healthcare costs while improving care quality and health outcomes. To achieve these goals, delivery systems must address the medical, social, and behavioral needs of populations with high-cost, complex conditions, particularly as upstream preventive services could preclude expensive complications.1,2 A blend of population-level data and innovative service models should be used to engage patients, address risk factors for poor outcomes, improve management of chronic disease, and reduce unnecessary acute care expenditures.3-7

Increasingly, Vermont is producing population-level data to support a learning health system, better care, and more informed oversight.8 The state’s data infrastructure includes an all-payer claims database (APCD) containing data from major commercial insurers, Medicaid, and Medicare. It also includes a statewide health information exchange network (HIEN) funded by the state of Vermont and developed and managed by the Vermont Information Technology Leaders (VITL), an independent nonprofit organization established in statute.9 One of the roles of the HIEN is to populate a state-maintained clinical registry with data from electronic health record (EHR) systems used in primary care medical homes and hospitals across the state. Every 6 months, extracts from the claims and clinical data systems are linked at the patient level to evaluate comparative performance in expenditures, utilization, quality, and clinical outcomes.10,11

This study examined how linked claims and clinical data from Vermont’s statewide data systems could inform a population care and health management model for high-cost, complex patients with common chronic conditions. The 3 objectives were: 1) developing a model to identify subpopulations through a set of selection criteria (ie, modifiable clinical risk factors and manageable comorbid conditions) regularly tracked in medical records for which primary care providers could improve preventive care, 2) estimating predictable impacts on healthcare costs upon achieving model goals within a 1- to 2-year period, and 3) setting care goals based on established guidelines and available treatments for selection criteria.

Individuals with diabetes in Vermont were selected as a subpopulation due to the disease’s prevalence, overlap with other common comorbid conditions, and impact on health and healthcare costs.12,13 Furthermore, the disease course of diabetes can be modified by addressing routinely tracked risk factors, such as glycemic control, blood pressure, and body mass index (BMI), and by improving control of common comorbid conditions with available treatments.14-17 In developing the model for a population with diabetes, this study demonstrated how statewide health data and information infrastructure could be used to identify patient subpopulations for targeted outreach and panel management and to improve proactive, preventive care. 



To achieve the objectives, the study evaluated whether glycemic control was associated with same-year expenditures. It also examined which clinical risk factors and comorbid conditions had strong associations with same-year expenditures and could serve as selection criteria for outreach. Finally, the study determined the rate of hospital admissions due to ambulatory care sensitive conditions (ACSCs)18 for those with the selection criteria and calculated the financial impact of reducing admissions through better care.

Data Sources and Study Population

Vermont’s APCD, which includes commercial, Medicaid, and Medicare eligibility and medical and pharmacy claims data for residents of Vermont, served as the study’s source for claims data. Detailed descriptions of this database have been published previously.19,20 A statewide clinical data registry provided clinical data from practice and hospital EHRs and included height, weight, blood pressure, and results from glycated hemoglobin (A1C) tests. Through a linkage process, claims data was paired with corresponding clinical data at the individual level.

The study population consisted of patients with diabetes identified by claims from January 1, 2014, to December 31, 2014, according to the Healthcare Effectiveness Data and Information Set specifications outlined in the Comprehensive Diabetes Care measure.21 These specifications included members aged 18 to 75 years with continuous enrollment during the study year and at least 1 acute inpatient visit or 2 outpatient visits indicating diabetes or who were dispensed insulin, hypoglycemics, or antihyperglycemics in the year prior to or during the study year. The study population was further limited to those whose claims data could be linked to clinical measures. 

Risk Factor and Outcome Measures

Risk factors and comorbidities in claims data included age, gender, disability status (Medicare), insulin use, and ACSCs, such as chronic obstructive pulmonary disease (COPD), asthma, congestive heart failure (CHF), coronary heart disease (CHD), depression, and renal failure. Clinical risk factors included BMI, blood pressure (diastolic and systolic), and A1C test results. Preliminary analysis indicated little variation in expenditures among BMI levels ≥18.5 and <35 (the “normal,” “overweight,” and “obese (class 1)” BMI categories).22 Thirty-five members classified as underweight (ie, BMI <18.5) were excluded from the analysis because confounding attributes, such as a high prevalence of renal failure, resulted in expenditure and admission trends inconsistent with those with <35 BMI. Therefore, members with BMI ≥35 (severe obesity classes 2 and 3) were compared with those with a BMI ≥18.5 or <35. Members with blood pressure not in control were grouped into 3 categories: high (systolic ≥140 mm Hg or diastolic ≥90 mm Hg), low (systolic ≤90 mm Hg or diastolic ≤60 mm Hg), and discordant (systolic ≥140 mm Hg and diastolic ≤60 mm Hg). Members with A1C not in control were classified as having high A1C (>9%). Other A1C categories included mid-range (>6% but ≤9%) and low (≤6.0%). The low-A1C group was identified through clinical data, which showed high costs associated with low A1C values, as seen in Figure 1. Findings in the literature indicated elevated risks among patients with diabetes with lower A1C and comorbidities.23,24 

Outcome measures for this study came from the claims data. Total expenditures encompassed allowed amounts on claims, including amounts paid by the insurer and member (eg, coinsurance, deductible, co-pays) for services in all settings (inpatient, outpatient facility, professional, ambulance, and pharmacy) and for durable medical equipment. Although Vermont Medicaid covers special nonmedical services targeted at meeting social, economic, and rehabilitative needs (eg, transportation, home- and community-based services, case management, dental services, residential treatment, mental health facilities, and school-based services), claims for these services were excluded to maintain consistency with services covered by other payers.

To analyze potential opportunities to reduce utilization and cost, acute inpatient hospitalizations and associated expenditures were measured for conditions listed under the Prevention Quality Indicators (PQIs) from the Agency for Healthcare Research and Quality, which included admissions for diabetes short-term complications, perforated appendix, diabetes long-term complications, COPD, asthma, hypertension, heart failure, dehydration, bacterial pneumonia, urinary tract infection, angina without procedure, uncontrolled diabetes, asthma, and lower extremity amputations.18 When guideline-based outpatient and preventive care is provided, hospitalization for these conditions is reduced.25 

Analytical Methods

Because the study population was limited to patients with diabetes with claims linked to clinical data, we compared the demographic characteristics of this group against that of patients with diabetes with only claims data to review whether limiting the sample population to those with linked data created a selection bias. Pearson’s χ2 test was used to compare the 2 groups.

Multivariable linear regression was used to determine the relative impact of each risk factor on expenditures. To reduce the impact of extreme outlier cases and to correct for skew in the expenditures, a log transformation was applied. For predictor variables, claims data provided information about demographics, payer type, and prevalence of comorbidities. The clinical data provided the most recent blood pressure, BMI, and A1C records. Using the full set of variables, stepwise selection identified which predictor variables to include in the regression model. 

The coefficients from the final regression model were used to calculate the relative effect on costs associated with each risk factor. Relative effects indicated a proportional change from the baseline category to the at-risk category while controlling for all other variables in the analysis. The relationship between the sum of the total costs of a population associated with a particular risk factor and the inverse of the relative effect (ie, the relative effect if the same population did not have the risk factor or condition) was used to estimate the additional cost associated with having the particular risk factor compared with not having the risk factor while controlling for other factors. This relationship is reflected in the below expression where x refers to the target risk factor: 
∑ Costsx − ∑ Costsx
Relative Effectx

A Poisson regression used the same predictor variables identified in the cost model with inpatient admissions as the response variable to estimate the relative effect of the risk factors on inpatient admissions. 

Potential savings from a reduction in ACSCs were determined using a 20% and 50% reduction in these hospitalizations, assuming the mean cost per hospitalization for eliminated hospitalizations was the same as the overall mean cost per hospitalization. These reductions were calculated for the entire study population and independently for each risk factor subpopulation. 
All statistical analysis was done with SAS version 9.3 (SAS Institute Inc; Cary, North Carolina). 

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