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The American Journal of Managed Care December 2019
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A Claims-Based Algorithm to Reduce Relapse and Cost in Schizophrenia
Heidi C. Waters, PhD, MBA; Charles Ruetsch, PhD; and Joseph Tkacz, MS
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Hussain S. Lalani, MD; Patti L. Ephraim, MPH; Arielle Apfel, MPH; Hsin-Chieh Yeh, PhD; Nowella Durkin; Lindsay Andon, MSPH; Linda Dunbar, PhD; Lawrence J. Appel, MD; and Felicia Hill-Briggs, PhD; for the Johns Hopkins Community Health Partnership
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A Claims-Based Algorithm to Reduce Relapse and Cost in Schizophrenia

Heidi C. Waters, PhD, MBA; Charles Ruetsch, PhD; and Joseph Tkacz, MS
Implementing a claims-based algorithm and disease management program may be an effective strategy to reduce relapse and cost among patients with schizophrenia.
ABSTRACT

Objectives: To refine a payer algorithm identifying patients with schizophrenia at high risk of relapse within a managed Medicaid population and evaluate its effectiveness in a case management (CM) program.

Study Design: Cross-sectional and longitudinal study design.

Methods: The algorithm used a single payer’s Medicaid medical and pharmacy claims (August 1, 2009, to July 31, 2014) for patients with schizophrenia (N = 12,353) to predict those at high risk for hospitalization. The final algorithm was used in a CM program (outbound communication to providers) at 3 payer service centers in 3 states. Based on the algorithm, 60 patients (20 from each site) with the highest risk scores were targeted for CM (CM group) and 60 (those patients ranked 21st-40th most at-risk at each site) comprised the control group. Chi-square tests compared groups on frequency measures (hospitalizations, emergency department [ED] visits). Pre- to postimplementation differences were tested using McNemar’s test. A pre–post analysis of variance assessed mean numbers of inpatient admissions, inpatient days, and ED visits for both groups.

Results: The algorithm had good positive predictive power (64.0%), negative predictive power (94.7%), sensitivity (40.2%), and specificity (97.9%). Following CM, the proportion of patients with at least 1 inpatient admission in the CM group decreased (23.3% to 13.3%), as did the rate of ED visits per month (by approximately 15%), whereas increases were observed in the control group.

Conclusions: Although not all of these differences were statistically significant, they suggest that the algorithm may be an effective case-finding tool for plans attempting to mitigate hospitalizations among high-risk patients with schizophrenia.

Am J Manag Care. 2019;25(12):e373-e378
Takeaway Points

We developed a regression-based payer algorithm to identify patients with schizophrenia at the greatest risk of relapse and hospitalization. Further, we assessed the effectiveness of this algorithm in a disease management program.
  • The algorithm developed in our study allows for a combination of multiple claims-based proxy measures of patient instability, predicting relapse with greater accuracy than a single measure of instability.
  • The algorithm had high negative predictive power and specificity for detecting, was able to detect lack of relapse risk accurately, and could also assess risk of relapse but not as strongly.
  • Although these differences were not statistically significant between intervention and control groups, the algorithm may be an effective case-finding tool for plans attempting to mitigate hospitalizations among high-risk patients with schizophrenia.
Schizophrenia is a chronic remitting and relapsing mental illness that affects approximately 1.1% of the US population and between 1% and 2% of the Medicaid population.1,2 Schizophrenia has one of the highest aggregate costs of all mental illnesses, estimated at $37.7 billion for direct medical costs and an additional $117 billion for indirect costs in the United States in 2013.3 Relapse of schizophrenia is the largest cost driver,4,5 with these patients incurring up to 5-fold more medical costs than those who do not experience relapse, largely due to the inpatient costs associated with relapse.4,6-8 Therefore, improved methods to identify patients at greatest risk of relapse may reduce the number of relapses and the use of associated high-cost services.9

There are many early warning signs that can precede relapse in patients with schizophrenia, including nonadherence to treatment, ongoing depression, poor therapeutic alliance, and persistent substance abuse.10-12 The presentation of schizophrenia can be heterogeneous and, therefore, some clinicians believe that schizophrenia is a clinical syndrome with different patterns of symptoms rather than a singular, uniform disease.13,14 One model available to payers to estimate trajectory to relapse is to use claims-based indicators of patient behaviors (eg, antipsychotic medication adherence) or consequences (eg, prior psychiatric hospitalization, emergency department [ED] visits).4,11 However, neither of these are effective as standalone predictors.15 By contrast, earlier research employed a predictive model that allowed any combination of 6 claims-based proxy measures of patient instability (ie, patient instability events [PIEs]). These 6 proxy measures were summed using unit weighting into a composite score, which predicted decompensation with more accuracy than any single measure alone in a commercially insured population.16 Use of composite measures may assist payers in identifying those patients at a high risk of relapse and targeting interventions to avoid costly relapses. Interventions designed to reduce relapse in patients with schizophrenia vary from intensive cognitive behavioral therapy to broader community-based psychoeducational formats.17,18

The goals of this study were (1) to translate and refine the PIE algorithm concept for use as a case-finding algorithm that identified patients with schizophrenia at greatest risk for relapse and hospitalization within a managed Medicaid population and (2) to evaluate the effectiveness of a case management (CM) program that included the case-finding algorithm and outreach to identified patients’ healthcare providers to help avoid relapse. The ultimate goal of the program was to reduce relapse rates and associated healthcare expenditures while improving patient outcomes.

METHODS

Case-Finding Algorithm

Data derived from a single payer’s Medicaid claims inclusive of medical and pharmacy claims from August 1, 2009, to July 31, 2014, were used to refine the schizophrenia case-finding algorithm originally developed using a commercial claims database. The data set included medical and pharmacy claims and eligibility data for all patients who had at least 1 inpatient or 2 outpatient claims containing a diagnosis for schizophrenia as identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 295.x or International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code F20.x between July 1, 2012, and June 30, 2013 (case-finding period), as well as continuous eligibility during both the case-finding and measurement (July 1, 2013, to June 30, 2014) periods (Figure 1). Excluded from the study sample were cases with diagnoses of schizophreniform disorder (ICD-9-CM code 295.4x or ICD-10-CM code F20.81) or schizotypal disorder (ICD-9-CM code 295.7x or ICD-10-CM code F25.90) in the absence of other forms of schizophrenia, based on consultation with the health plan clinical leads.

Medicaid claims were used to assess the relationship between instability of patients with schizophrenia and relapse during the study period to identify indicators of instability and combinations that adequately predict patient relapse. Eight proxies of instability—6 of which were identified in a prior study16 and 2 of which were added at the request of clinical staff at the payer—were computed for each of the first 2 quarters of the measurement period (predictor assessment period) and were used in linear combination to detect subsequent relapse: Charlson Comorbidity Index (CCI) score; medication switch, defined as receipt of an antipsychotic medication other than the index antipsychotic medication; count of hospital admissions with a primary diagnosis of schizophrenia; count of ED visits with a psychiatric diagnosis (ICD-9-CM codes 295, 296, 300-305; ICD-10-CM codes F209, F200, F201, F202, F203, F205, F208, F2081, F2089, F250, F251, F258, F259, F39, F319, F419, F21, F609, F659, F102, F112, F1219, F1319, F1499, F1519, F1699, F1819, F1019); any fill for a psychiatric medication other than an antipsychotic; presence of a diagnosis of depression (ICD-9-CM codes 296.2x, 296.3x, 300.4x, 309.1x, 311.x; ICD-10-CM codes F329, F3340, F349, F4321); presence of a diagnosis of bipolar disorder (ICD-9-CM code 296.x except 296.2x and 296.3x; ICD-10-CM codes F209, F200, F201, F202, F203, F205, F208, F2081, F2089, F250, F251, F258, F259); and presence of a diagnosis of psychosis other than the primary diagnosis (ie, nonorganic psychosis diagnosis).


 
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