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Chaos to complexity: leveling the playing field for measuring value in primary care
Author(s) -
Moran William P.,
Zhang Jingwen,
Gebregziabher Mulugeta,
Brownfield Elisha L.,
Davis Kimberly S.,
Schreiner Andrew D.,
Egan Brent M.,
Greenberg Raymond S.,
Kyle T. Rogers,
Marsden Justin E.,
Ball Sarah J.,
Mauldin Patrick D.
Publication year - 2017
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.12298
Subject(s) - medicine , confidence interval , population , cluster (spacecraft) , cohort , emergency department , primary care , kidney disease , demography , emergency medicine , family medicine , environmental health , psychiatry , sociology , computer science , programming language
Rationale, aims and objectives Develop a risk‐stratification model that clusters primary care patients with similar co‐morbidities and social determinants and ranks ‘within‐practice’ clusters of complex patients based on likelihood of hospital and emergency department ( ED ) utilization. Methods A retrospective cohort analysis was performed on 10 408 adults who received their primary care at the M edical U niversity of S outh C arolina U niversity I nternal M edicine clinic. A two‐part generalized linear regression model was used to fit a predictive model for ED and hospital utilization. Agglomerative hierarchical clustering was used to identify patient subgroups with similar co‐morbidities. Results Factors associated with increased risk of utilization included specific disease clusters {e.g. renal disease cluster [rate ratio, RR = 5.47; 95% confidence interval ( CI ; 4.54, 6.59) P < 0.0001]}, low clinic visit adherence [ RR = 0.33; 95% CI (0.28, 0.39) P < 0.0001] and census measure of high poverty rate [ RR = 1.20; 95% CI (1.11, 1.28) P < 0.0001]. In the cluster model, a stable group of four clusters remained regardless of the number of additional clusters forced into the model. Although the largest number of high‐utilization patients (top 20%) was in the multiple chronic condition cluster (1110 out of 4728), the largest proportion of high‐utilization patients was in the renal disease cluster (67%). Conclusions Risk stratification enhanced with disease clustering organizes a primary care population into groups of similarly complex patients so that care coordination efforts can be focused and value of care can be maximized.