Clinical Integration of a Smartphone App for Patients With Chronic Pain: Retrospective Analysis of Predictors of Benefits and Patient Engagement Between Clinic Visits.

BACKGROUND: Although many pain-related smartphone apps exist, little attention has been given to understanding how these apps are used over time and what factors contribute to greater compliance and patient engagement. OBJECTIVE: This retrospective analysis was designed to help identify factors that predicted the benefits and future use of a smartphone pain app among patients with chronic pain. METHODS: An app designed for both Android and iOS devices was developed by Brigham and Women's Hospital Pain Management Center (BWH-PMC) for users with chronic pain to assess and monitor pain and communicate with their providers. The pain app offered chronic pain assessment, push notification reminders and communication, personalized goal setting, relaxation sound files, topics of interest with psychological and medical pain management strategies, and line graphs from daily assessments. BWH-PMC recruited 253 patients with chronic pain over time to use the pain app. All subjects completed baseline measures and were asked to record their progress every day using push notification daily assessments. After 3 months, participants completed follow-up questionnaires and answered satisfaction questions. We defined the number of completed daily assessments as a measure of patient engagement with the pain app. RESULTS: The average age of participants was 51.5 years (SD 13.7, range 18-92), 72.8% (182/253) were female, and 36.8% (78/212) reported the low back as their primary pain site. The number of daily assessments ranged from 1 to 426 (average 62.0, SD 49.9). The app was easy to introduce among patients, and it was well accepted. Those who completed more daily assessments (greater patient engagement) throughout the study were more likely to report higher pain intensity, more activity interference, and greater disability and were generally overweight compared with others. Patients with higher engagement with the app rated the app as offering greater benefit in coping with their pain and expressed more willingness to use the app in the future (P<.05) compared with patients showing lower engagement. Patients completing a small number of daily assessments reported less pain intensity, less daily activity interference, and less pain-related disability on average and were less likely to use the two-way messaging than those who were more engaged with the pain app (P<.05). CONCLUSIONS: Patients with chronic pain who appeared to manage their pain better were less likely to report benefits of a smartphone pain app designed for chronic pain management. They demonstrated lower patient engagement in reporting their daily progress, in part, owing to the perceived burden of regularly using an app without a perceived benefit. An intrinsically different pain app designed and targeted for individuals based on early identification of user characteristics and adapted for each individual would likely improve compliance and app-related patient engagement.

An interrupted time series study of electronic health record clinical decision support for providers caring for patients with atrial fibrillation at increased stroke risk.

PURPOSE: To measure the effect of clinical decision support (CDS) on anticoagulation rates in patients with atrial fibrillation (AFib) or atrial flutter (AFlut) at high stroke risk and receiving care in outpatient settings, and to assess provider response to CDS. METHODS: This observational, quasi-experimental, interrupted time series study utilized electronic health record data at a large integrated delivery network in Texas from April to November 2020. CDS consisted of an electronic Best Practice Advisory (BPA)/alert (Epic Systems Corporation, Verona, WI) with links to 2 AFib order sets displayed to providers in outpatient settings caring for non-anticoagulated patients with AFib and elevated CHA2DS2VASc scores. Weekly outpatient anticoagulation rates were assessed in patients with high stroke risk before and after implementation of CDS. Alert actions and acknowledgment reasons were evaluated descriptively. RESULTS: Mean (SD) weekly counts of eligible patients were 8,917 (566) before and 8,881 (811) after implementation. Weekly anticoagulation rates increased during the pre-BPA study period (ß1 = 0.07%; SE, 0.02%; P = 0.0062); however, there were no significant changes in the level (ß2 = 0.60%; SE, 0.42%; P = 0.1651) or trend (ß3 = -0.01%; SE, 0.05%; P = 0.8256) of anticoagulation rates associated with CDS implementation. In encounters with the BPA/alert displayed (n = 17,654), acknowledgment reasons were provided in 4,473 (25.3%) of the encounters, with prescribers most commonly citing bleeding risk (n = 1,327, 7.5%) and fall risk (n = 855, 4.8%). CONCLUSION: There was a significant trend of increasing anticoagulation rates during the pre-BPA period, with no significant change in trend during the post-BPA period relative to the pre-BPA period.

Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center.

Background: Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently under-recognized cause of heart failure (HF) in older patients. To improve identification of patients at risk for the disease, we initiated a pilot program in which 9 cardiac/non-cardiac phenotypes and 20 high-performing phenotype combinations predictive of wild-type ATTR-CM were operationalized in electronic health record (EHR) configurations at a large academic medical center. Methods: Inclusion criteria were age >50 years and HF; exclusion criteria were end-stage renal disease and prior amyloidosis diagnoses. The different Epic EHR configurations investigated were a clinical decision support tool (Best Practice Advisory) and operational/analytical reports (Clarity™, Reporting Workbench™, and SlicerDicer); the different data sources employed were problem list, visit diagnosis, medical history, and billing transactions. Results: With Clarity, among 45 051 patients with HF, 4006 patients (8.9%) had ⩾1 phenotype combination associated with increased risk of wild-type ATTR-CM. Across all data sources, 2 phenotypes (cardiomegaly; osteoarthrosis) and 2 combinations (carpal tunnel syndrome + HF; atrial fibrillation + heart block + cardiomegaly + osteoarthrosis) generated the highest proportions of patients for wild-type ATTR-CM screening. Conclusion: All EHR configurations tested were capable of operationalizing phenotypes or phenotype combinations to identify at-risk patients; the Clarity report was the most comprehensive.

Usability, Perceived Usefulness, and Shared Decision-Making Features of the AFib 2gether Mobile App: Protocol for a Single-Arm Intervention Study.

BACKGROUND: The Centers for Disease Control and Prevention has estimated that atrial fibrillation (AF) affects between 2.7 million and 6.1 million people in the United States. Those who have AF tend to have a much higher stroke risk than others. Although most individuals with AF benefit from anticoagulation (AC) therapy, a significant majority are hesitant to start it. To add, providers often struggle in helping patients negotiate the decision to start AC therapy. To assist in the communication between patients and providers regarding preferences and knowledge about AC therapy, different strategies are being used to try and solve this problem. In this research study, we will have patients and providers utilize the AFib 2gether app with hopes that it will create a platform for shared decision making regarding the prevention of stroke in patients with AF receiving AC therapy. OBJECTIVE: The aim of our study is to measure several outcomes related to encounters between patients and their cardiology providers where AFib 2gether is used. These outcomes include usability and perceived usefulness of the app from the perspective of patients and providers. In addition, we will assess the extent and nature of shared decision making. METHODS: Eligible patients and providers will evaluate the AFib 2gether mobile app for usability and perceived usefulness in facilitating shared decision making regarding understanding the patient's risk of stroke and whether or not to start AC therapy. Both patients and providers will review the app and complete multiple questionnaires about the usability and perceived usefulness of the mobile app in a clinical setting. We will also audio-record a subset of encounters to assess for evidence of shared decision making. RESULTS: Enrollment in the AFib 2gether shared decision-making study is still ongoing for both patients and providers. The first participant enrolled on November 22, 2019. Analysis and publishing of results are expected to be completed in spring 2021. CONCLUSIONS: The AFib 2gether app emerged from a desire to increase the ability of patients and providers to engage in shared decision making around understanding the risk of stroke and AC therapy. We anticipate that the AFib 2gether mobile app will facilitate patient discussion with their cardiologist and other providers. Additionally, we hope the study will help us identify barriers that providers face when placing patients on AC therapy. We aim to demonstrate the usability and perceived usefulness of the app with a future goal of testing the value of our approach in a larger sample of patients and providers at multiple medical centers across the country. TRIAL REGISTRATION: ClinicalTrials.gov NCT04118270; https://clinicaltrials.gov/ct2/show/NCT04118270. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21986.