Emergency Department Utilization and Hierarchical Condition Category Risk Scores

BACKGROUND: The Centers for Medicare and Medicaid Services (CMS) introduced Comprehensive Primary Care Plus (CPC+) to improve the quality of primary care services nationwide. CPC+ utilization measures use a risk-adjustment model to predict utilization for different patient populations. Risk is determined using Hierarchical Condition Categories (HCCs), which are based on ICD-10 codes and patient demographics. Since patients with higher HCC scores are expected to have higher utilization rates, CMS uses these calculations to compare practices and categorize patients into “risk tiers,” which guide payments. OUSCM participates in CPC+ (Track 2). In order to track our patients’ emergency department utilization (EDU), search for patterns of use, and identify opportunities for quality improvement, we sought to determine (1) what associations exist between HCC risk tiers and patterns of EDU and (2) what patient characteristics are associated with HCC risk scores. METHODS: We analyzed cross-sectional CPC+ data for fiscal year 2018 provided by CMS. We performed multiple linear regression, Tukey’s method, and independent-samples t-tests to explore possible relationships between EDU, HCC risk score and associated risk tiers (range 1-5), and patient characteristics, such as dual-eligibility status and age. The study population included 906 Medicare-only and 1173 dual-eligible patients aged 18 years and older attributed to the OUSCM Internal Medicine (n=1122) or Family Medicine (n=957) practice. RESULTS: Our patient population had a median HCC risk score of 0.93 (CMS-reported Oklahoma median risk score = 0.74). We found that 56.4% of our patients were dual-eligible compared to the national average of 19.4%. Tukey multiple comparison test demonstrated significant differences between risk tiers and ED visits (p < .05). Dual-eligible patients had a higher average HCC risk score than Medicare-only patients (t(2072) = 8.491; p < .00001) and a higher average number of ED visits (t(2077) = 3.9577; p < .00001). Age was weakly correlated with HCC risk scores (r = .074, p = .0228). Density analysis of HCC scores by age revealed evidence of low-risk clustering for adults between 45 and 75 years of age. CONCLUSION: HCC risk tier classifications are predictive of EDU rates in our patient population. However, our overall HCC score was lower than anticipated given the complexity of our patient population. Dual-eligible status was associated with higher risk and EDU rates. However, age–typically an independent predictor of morbidity and mortality–was only weakly correlated with HCC scores, suggesting clinicians may be undercoding encounters for adults between the ages of 45 and 75 years, which decreases revenue.N

High Crime Neighborhoods as a Driver for Toxic Stress Leading to Asthma

BACKGROUND: Social determinants of health and allostatic load theory suggest social environment can drive asthma diagnoses via the mechanism of toxic stress, the prolonged activation of stress response systems. While research has linked neighborhood crime to asthma, multivariate causal modeling has not been used to test toxic stress as the mechanism that links the two. The current study investigates neighborhood crime as a driver of pediatric asthma diagnoses via toxic stress. METHODS: A retrospective geospatial analysis of health and crime data was conducted. Health data was collected from the OU-Tulsa General Pediatric Clinic’s Electronic Medical Record while crime data was collected from the Tulsa Police Department. All variables were mapped geospatially using census tract as the unit of analysis. Structural equation modeling was used to test the causal model. Neighborhood crime indicators included homicide, rape, and narcotic-related offenses. Diagnoses of conduct, attention deficit, and other anxiety disorders were used in the analysis as toxic stress indicators. Asthma diagnoses were the outcome variable. To further test the model, data from 2016 was used as a calibration sample while data from 2017 was used as a validation sample. RESULTS: A full mediation model of high crime neighborhoods (n = 134) as a driver of toxic stress resulting in increased asthma diagnoses fit the 2016 data well (Χ2 = 15.6, p =.27; df = 13; RMSEA = .04 [90% CI: .00, .10]; CFI: .99; SRMR = .04). The results indicated the model accounted for 78% (R2 = .78) of the variance in asthma diagnoses. The model also provided a good fit to the 2017 data (X2= 23.6, p<.001; df= 13; RMSEA = .08 [90% CI: .02, .13]; CFI: .96; SRMR=.06). CONCLUSION: The results of the current study have important practice and research implications. While clinicians and researchers have become increasingly aware of the impact of social determinants of health, there has been little focus on improving clinical practices. Physicians interested in alleviated the burden of toxic stress and asthma should explore ways to reduce neighborhood crime at the policy level while also being aware of each of their patients’ unique circumstances in relation to where they live.N

Variation and Change Over Time in PROMIS-29 Survey Results Among Primary Care Patients With Type 2 Diabetes

Purpose: We sought to describe results of patient-reported outcome measures implemented among primary care patients with diabetes and explore factors associated with changes in scores over time. Methods: Two organizations serving diverse patient populations collected the PROMIS-29 survey at baseline and 3-month follow-up for patients with type 2 diabetes. Bayesian regression analysis was used to examine the relationship between patient characteristics and changes in PROMIS-29 scores. Exploratory analyses assessed relationships between goal-setting and changes in scores. Results: The study population reported substantially more problems with physical functioning (mean: 42.5 at Site 1 and 38.9 at Site 2) and pain interference (mean: 58.0 at Site 1 and 61.1 at Site 2) compared to the general population (mean: 50; standard deviation: 10). At least 33% of patients had a clinically meaningful change (ie, at least half the standard deviation, or 5 points) in each PROMIS domain. For pain interference, 55% had no change, 22% improved by 5 or more points, and 23% worsened by 5 or more points. Bayesian regression analyses suggest that chronic conditions, insurance status, and Hispanic ethnicity are likely associated with decreased functioning over time. Exploratory analyses found that setting a mental health goal did not appear to be associated with improvement for anxiety or depression. Conclusions: Use of patient-reported outcome measures in routine clinical care identified areas of functional limitations among people with diabetes. However, changes in participants’ PROMIS-29 scores over time were minimal. Research is needed to understand patterns of change in global and domain-specific functioning, particularly among racial/ethnic minorities

Big Data in the Big D

Presented at the 2017 International Conference on Knowledge Management. This presentation examines performance measurements in healthcare and misclassification error on estimated cardiovascular care performance

DO NO HARM: PAIN AND OPIOID MANAGEMENT IN OKLAHOMA PRIMARY CARE

Our study is about a dissemination and implementation program aimed at improving opioid prescribing guidelines in Oklahoma Primary Care practices

Variation and Change Over Time in PROMIS-29 Survey Results Among Primary Care Patients With Type 2 Diabetes

Purpose: We sought to describe results of patient-reported outcome measures implemented among primary care patients with diabetes and explore factors associated with changes in scores over time. Methods: Two organizations serving diverse patient populations collected the PROMIS-29 survey at baseline and 3-month follow-up for patients with type 2 diabetes. Bayesian regression analysis was used to examine the relationship between patient characteristics and changes in PROMIS-29 scores. Exploratory analyses assessed relationships between goal-setting and changes in scores. Results: The study population reported substantially more problems with physical functioning (mean: 42.5 at Site 1 and 38.9 at Site 2) and pain interference (mean: 58.0 at Site 1 and 61.1 at Site 2) compared to the general population (mean: 50; standard deviation: 10). At least 33% of patients had a clinically meaningful change (ie, at least half the standard deviation, or 5 points) in each PROMIS domain. For pain interference, 55% had no change, 22% improved by 5 or more points, and 23% worsened by 5 or more points. Bayesian regression analyses suggest that chronic conditions, insurance status, and Hispanic ethnicity are likely associated with decreased functioning over time. Exploratory analyses found that setting a mental health goal did not appear to be associated with improvement for anxiety or depression. Conclusions: Use of patient-reported outcome measures in routine clinical care identified areas of functional limitations among people with diabetes. However, changes in participants’ PROMIS-29 scores over time were minimal. Research is needed to understand patterns of change in global and domain-specific functioning, particularly among racial/ethnic minorities

Additional file 1: of Disseminating, implementing, and evaluating patient-centered outcomes to improve cardiovascular care using a stepped-wedge design: healthy hearts for Oklahoma

H2O Practice Characteristics Survey. (PDF 700 kb

Additional file 2: of Disseminating, implementing, and evaluating patient-centered outcomes to improve cardiovascular care using a stepped-wedge design: healthy hearts for Oklahoma

H2O Practice Member Survey. (PDF 784 kb