BackgroundAs the capabilities and reach of technology have expanded, there is an accompanying proliferation of digital technologies developed for use in the care of patients with mental illness. The objective of this review was to systematically search published literature to identify currently available health technologies and their intended uses for patients with serious mental illness.Materials and methodsThe Medline, Embase, and BIOSIS Previews electronic databases were searched to identify peer-reviewed English language articles that reported the use of digital, mobile, and other advanced technology in patients with schizophrenia/schizoaffective disorder, bipolar disorder, and major depressive disorder. Eligible studies were systematically reviewed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.ResultsEighteen studies that met the inclusion criteria were identified. Digital health technologies (DHTs) assessed in the selected studies included mobile applications (apps), digital medicine, digital personal health records, and an electronic pill container. Smartphone apps accounted for the largest share of DHTs. The intended uses of DHTs could be broadly classified as monitoring to gain a better understanding of illness, clinical assessment, and intervention. Overall, studies indicated high usability/feasibility and efficacy/effectiveness, with several reporting validity against established clinical scales. Users were generally engaged with the DHT, and mobile assessments were deemed helpful in monitoring disease symptoms.ConclusionRapidly proliferating digital technologies seem to be feasible for short-term use in patients with serious mental illness; nevertheless, long-term effectiveness data from naturalistic studies will help demonstrate their usefulness and facilitate their adoption and integration into the mental health-care system.

Otsuka Pharmaceutical Development & Commercialization provided support for this research. MacEwan and Shafrin are employees of Precision Health Economics, which was contracted by Otsuka Pharmaceutical Development & Commercialization to conduct this study. Lakdawalla is the Chief Scientific Officer and a founding partner of Precision Health Economics. Forma is an employee of Otsuka Pharmaceutical Development & Commercialization. Hatch is a former employee of Otsuka Pharmaceutical Development & Commercialization and is a current employee of ODH, Inc. Lindenmayer has received grant/research support from Janssen, Lilly, AstraZeneca, Johnson & Johnson, Pfizer, BMS, Otsuka, Dainippon, and Roche and is a consultant for Janssen, Lilly, Merck, Shire, and Lundbeck. Portions of this study were presented as a poster at the American Society of Clinical Psychopharmacology Annual Meeting in Miami Beach, Florida; June 23, 2015; and at the 28th Annual U.S. Psychiatric and Mental Health Congress; San Diego, California; September 12, 2015. Study concept and design were contributed by Forma, Ladkawalla, MacEwan, and Shafrin, along with Hatch and Lindenmayer. MacEwan, Shafrin, Forma, and Lakdawalla collected the data, along with Hatch and Lindenmayer. Data interpretation was performed by Hatch, Lindenmayer, MacEwan, and Shafrin, assisted by Forma and Lakdawalla. The manuscript was written and revised by MacEwan, Forma, and Shafrin, along with Hatch Lakdawalla, and Lindenmayer.

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IntroductionPatients with mental and physical health conditions are complex to treat and often use multiple medications. It is unclear how adherence to one medication predicts adherence to others. A predictive relationship could permit less expensive adherence monitoring if overall adherence could be predicted through tracking a single medication.MethodsTo test this hypothesis, we examined whether patients with multiple mental and physical illnesses have similar adherence trajectories across medications. Specifically, we conducted a retrospective cohort analysis using health insurance claims data for enrollees who were diagnosed with a serious mental illness, initiated an atypical antipsychotic, as well as an SSRI (to treat serious mental illness), biguanides (to treat type 2 diabetes), or an ACE inhibitor (to treat hypertension). Using group-based trajectory modeling, we estimated adherence patterns based on monthly estimates of the proportion of days covered with each medication. We measured the predictive value of the atypical antipsychotic trajectories to adherence predictions based on patient characteristics and assessed their relative strength with the R-squared goodness of fit metric.ResultsWithin our sample of 431,591 patients, four trajectory groups were observed: non-adherent, gradual discontinuation, stop–start, and adherent. The accuracy of atypical antipsychotic adherence for predicting adherence to ACE inhibitors, biguanides, and SSRIs was 44.5, 44.5, and 49.6%, respectively (all p < 0.001 vs. random). We also found that information on patient adherence patterns to atypical antipsychotics was a better predictor of patient adherence to these three medications than would be the case using patient demographic and clinical characteristics alone.ConclusionAmong patients with multiple chronic mental and physical illnesses, patterns of atypical antipsychotic adherence were useful predictors of adherence patterns to a patient’s adherence to ACE inhibitors, biguanides, and SSRIs.FundingOtsuka Pharmaceutical Development & Commercialization, Inc.Electronic supplementary materialThe online version of this article (10.1007/s12325-018-0700-6) contains supplementary material, which is available to authorized users.