title: Estimating the Value of New Technologies That Provide More Accurate Drug Adherence Information to Providers for Their Patients with Schizophrenia.
abstract: BACKGROUND
  Nonadherence to antipsychotic medication among patients with schizophrenia results in poor symptom management and increased health care and other costs. Despite its health impact, medication adherence remains difficult to accurately assess. New technologies offer the possibility of real-time patient monitoring data on adherence, which may in turn improve clinical decision making. However, the economic benefit of accurate patient drug adherence information (PDAI) has yet to be evaluated.
  
  
  OBJECTIVE
  To quantify how more accurate PDAI can generate value to payers by improving health care provider decision making in the treatment of patients with schizophrenia.
  
  
  METHODS
  A 3-step decision tree modeling framework was used to measure the effect of PDAI on annual costs (2016 U.S. dollars) for patients with schizophrenia who initiated therapy with an atypical antipsychotic. The first step classified patients using 3 attributes: adherence to antipsychotic medication, medication tolerance, and response to therapy conditional on medication adherence. The prevalence of each characteristic was determined from claims database analysis and literature reviews. The second step modeled the effect of PDAI on provider treatment decisions based on health care providers' survey responses to schizophrenia case vignettes. In the survey, providers were randomized to vignettes with access to PDAI and with no access. In the third step, the economic implications of alternative provider decisions were identified from published peer-reviewed studies. The simulation model calculated the total economic value of PDAI as the difference between expected annual patient total cost corresponding to provider decisions made with or without PDAI.
  
  
  RESULTS
  In claims data, 75.3% of patients with schizophrenia were found to be nonadherent to their antipsychotic medications. Review of the literature revealed that 7% of patients cannot tolerate medication, and 72.9% would respond to antipsychotic medication if adherent. Survey responses by providers (n = 219) showed that access to PDAI would significantly alter treatment decisions for nonadherent or adherent/poorly controlled patients (P < 0.001). Payers can expect to save $3,560 annually per nonadherent patient who would respond to therapy if adherent. Savings increased to $9,107 per nonadherent patient when PDAI was given to providers who frequently augmented therapy for these patients. Among all poorly controlled patients (i.e., the nonadherent or those who were adherent but unresponsive to therapy), access to PDAI decreased annual patient cost by $2,232. Savings for this group increased to $7,124 per patient when PDAI was given to providers who frequently augmented therapy.
  
  
  CONCLUSIONS
  Access to PDAI significantly improved provider decision making, leading to lower annual health care costs for patients who were nonadherent or adherent but poorly controlled. Additional research is warranted to evaluate how new technologies that accurately monitor adherence would affect health and economic outcomes among patients with serious mental illness.
  
  
  BACKGROUND
  This study and medical writing assistance was funded by Otsuka Pharmaceutical Development & Commercialization. Shafrin and Schwartz are employees of Precision Health Economics, which received funding from Otsuka Pharmaceutical Development & Commercialization in support of this study. Lakdawalla is Chief Scientific Officer and a founding partner of Precision Health Economics. Schwartz is a consultant for Otsuka Pharmaceutical Development & Commercialization, and Forma is an employee of Otsuka Pharmaceutical Development & Commercialization. The authors presented the abstract for this study as a poster presentation at the AMCP Managed Care & Specialty Pharmacy Annual Meeting, April 19-22, 2016, San Francisco, California. All authors contributed equally to the study design, data collection and analysis, and the writing and revision of the manuscript.
authors: 1 Precision Health Economics, Los Angeles, California.
authors: Jason
authors: J
authors: Shafrin
authors: 1 Precision Health Economics, Los Angeles, California.
authors: Taylor T
authors: TT
authors: Schwartz
authors: 2 Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles.
authors: Darius N
authors: DN
authors: Lakdawalla
authors: 3 Otsuka Pharmaceutical Development & Commercialization, Princeton, New Jersey.
authors: Felicia M
authors: FM
authors: Forma
mesh_terms: D014150:Antipsychotic Agents / Q000627:therapeutic use*; D003663:Decision Trees; D005260:Female; D006282:Health Personnel / Q000191:economics* / Q000639:trends; D006801:Humans; D008297:Male; D055118:Medication Adherence; D012559:Schizophrenia / Q000188:drug therapy* / Q000191:economics* / Q000453:epidemiology
chemical_list: D014150:Antipsychotic Agents