BackgroundThe explosion of mobile phones with app capabilities coupled with increased expectations of the patient-consumers’ role in managing their care presents a unique opportunity to use mobile health (mHealth) apps.ObjectivesThe aim of this paper is to identify the features and characteristics most-valued by patient-consumers (“users”) that contribute positively to the rating of an app.MethodsA collection of 234 apps associated with reputable health organizations found in the medical, health, and fitness categories of the Apple iTunes store and Google Play marketplace was assessed manually for the presence of 12 app features and characteristics. Regression analysis was used to determine which, if any, contributed positively to a user’s rating of the app.ResultsAnalysis of these 12 features explained 9.3% (R 2=.093 n=234, P<.001) of the variation in an app’s rating, with only 5 reaching statistical significance. Of the 5 reaching statistical significance, plan or orders, export of data, usability, and cost contributed positively to a user’s rating, while the tracker feature detracted from it.ConclusionsThese findings suggest that users appreciate features that save time over current methods and identify an app as valuable when it is simple and intuitive to use, provides specific instructions to better manage a condition, and shares data with designated individuals. Although tracking is a core function of most health apps, this feature may detract from a user’s experience when not executed properly. Further investigation into mHealth app features is worthwhile given the inability of the most common features to explain a large portion of an app’s rating. In the future, studies should focus on one category in the app store, specific diseases, or desired behavior change, and methods should include measuring the quality of each feature, both through manual assessment and evaluation of user reviews. Additional investigations into understanding the impact of synergistic features, incentives, social media, and gamification are also warranted to identify possible future trends.

Objective: To develop an economic model to evaluate changes in healthcare costs driven by restricting usage of branded tyrosine kinase inhibitors (TKIs) through substitution with generic imatinib among chronic myeloid leukemia (CML) patients in a typical Oncology Care Model (OCM) practice, and examine the impact on Performance-Based Payment (PBP) eligibility. Methods: An Excel-based economic model of an OCM practice with 1,000 cancer patients during a 6-month episode of care was developed. Cancer types and proportions of patients treated in the practice were estimated from an OCM report. All-cause healthcare costs were obtained from published literature. It was assumed that if a practice restricts usage of branded TKIs for newly-diagnosed CML patients, 80% of the market share of branded imatinib and 50% of the market shares of 2 nd-gen TKIs would shift to generic imatinib. Among established TKI-treated patients, it was assumed that 80% of the market share of branded imatinib and no patients treated with 2 nd-gen TKIs would shift to the generic. Results: Four CML patients were estimated for a 1,000-cancer patient OCM practice with a total baseline healthcare cost of $51,345,812 during a 6-month episode. If the practice restricts usage of branded TKIs, the shift from 2 nd-gen TKIs to generic imatinib would reduce costs by $12,970, while shifting from branded to generic imatinib lowers costs by $25,250 during a 6-month episode. Minimum reductions of $3,013,832 in a one-sided risk model and $2,372,010 in a two-sided risk model are required for PBP eligibility; the shift from 2 nd-gen TKIs to generic imatinib would account for 0.4% and 0.5% of the savings required for a PBP, respectively. Conclusions: This analysis indicates that the potential cost reduction associated with restricting branded TKI usage among CML patients in an OCM setting will represent only a small proportion of the cost reduction needed for PBP eligibility.

Introduction: In an Oncology Care Model (OCM) setting, practices may earn a Performance-Based Payment (PBP) for a reduction in the costs of treating participating Medicare patients during a 6-month episode of care. An Excel-based decision analytics model was developed to evaluate the cost-savings associated with implementing changes in the usage of tyrosine kinase inhibitors (TKIs) among patients with chronic myeloid leukemia (CML) within a typical OCM practice and the impact it could have on a practice potentially receiving a PBP. Methods: The default scenario is based on an OCM practice that treats 1,000 cancer patients during a 6-month episode of care. The types of cancers treated and the proportions of patients treated in the OCM practice were estimated from an OCM baseline report; all-cause healthcare costs for each cancer type were obtained from published literature. CML patients were stratified into newly-diagnosed and established TKI-treated patients. The percentages of CML patients on each of the TKIs (branded and generic imatinib [1st-gen TKIs], as well as dasatinib and nilotinib [2nd-gen TKIs]) within each stratum were estimated using market share data from April 2018. The 2018 Wholesales Acquisition Costs for the TKIs were obtained from RedBook. It was assumed that, if a practice implements the policy of restricting utilization of branded TKIs as a cost-cutting measure, 80% of the current market share of branded imatinib would shift to the generic and 50% of the current market shares of 2nd-gen TKIs would shift to generic imatinib. Among established TKI-treated patients, it was assumed that 80% of the current market share of branded imatinib would shift to the generic, whereas no patients treated with 2nd-gen TKIs would be switched to generic imatinib due to the lack of supporting evidence, physician and patient apprehension, some patients already having used imatinib, among other reasons. The relationship between the savings achieved from restricting utilization of 2nd-gen TKIs and the savings required for the OCM practice to receive a PBP using either a one-sided or two-sided risk model was evaluated. Results: The total healthcare costs of an OCM practice that treats 1,000 cancer patients for 6 months were estimated at $51,345,812. It was estimated that there would only be 4 CML patients in a 1,000-patient OCM practice, 1 newly-diagnosed and 3 established TKI-treated patients. Implementing the policy of restricting utilization of 2nd-gen TKIs for patients with CML would save a practice $12,970 during the 6-month episode of care, while $25,250 would be saved through a branded to generic imatinib shift (Table). For a 1,000-patient OCM practice participating in a one-sided risk model, a total cost-savings of $3,013,832 is required for it to be eligible for a PBP. In this scenario, the cost reduction associated with a shift from 2nd-gen TKIs to generic imatinib amounts to only 0.4% of the required total cost-savings threshold before the practice is eligible for a PBP. For a 1,000-patient OCM practice participating in a two-sided risk model, a total cost-savings of $2,372,010 is required for it to be eligible for a PBP. In this case, the cost reduction associated with a shift from 2nd-gen TKIs to generic imatinib amounts to only 0.5% of the required total cost-savings threshold before the practice is eligible for a PBP. Conclusions: This economic model indicates that the cost-savings associated with restricting branded TKI utilization among CML patients in an OCM setting will represent only a very small portion of the cost-savings required before an OCM practice is eligible for a PBP. Of the reduction in TKI costs, approximately two-thirds was attributed to the shift from branded to generic imatinib. Restricting utilization of the 2nd-gen TKIs contributed a negligible amount of savings required for a PBP. The cost-savings opportunities in CML in the OCM setting are limited by how few CML patients would be affected by restrictions. Disclosures Jabbour: Pfizer: Consultancy, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Abbvie: Research Funding. Mendiola:Bristol-Myers Squibb: Employment. Lingohr-Smith:Novosys Health: Employment. Menges:Novosys Health: Employment. Lin:Bristol-Myers Squibb: Consultancy; Novosys Health: Employment. Makenbaeva:Bristol-Myers Squibb: Employment.