AI in Healthcare Newsletter from January 28, 2019
Telehealth and Messaging for Post-Operative Care Works
Several studies in support of messaging, voice, and other applications of telehealth have been published in this first month of 2019. One study tests automated messaging for post-operative care and observed better mood, quicker stoppage of prescribed narcotic medication usage, more time spent with rehabilitative exercise. Another study of pharmacist led follow-up telemedicine calls led to positive results on hospital costs incurred by patients, mean time to readmission, and length of stay once readmitted.
Studies on telemedicine have been published in past years. A 2018 study on post-discharge complications among patients of arterial revascularization surgery found that patients receiving telemedicine reported much greater satisfaction.
As the efficacy of telehealth becomes clearer by the numbers, hospitals and health systems may find greater incentive to explore and implement a telehealth strategy.
Comprehensive Healthcare Platforms are the Future
Healthcare has begun its metamorphosis to a value-based model. With this change comes the opportunity to engage patients, improve population health, manage chronic disease, and offer accessibility and scale never before realized. With this change also comes the challenge of building the organization and platform that can offer these innovative services to the evolving demands of patients. 25 healthcare delivery systems spoke of this and more at the JP Morgan Healthcare Conference 2019.
Value-Based Incentives Lead to Unique Tech Adoption Mix
Research shows that between 2012 and 2016, hospitals with more payments based on quality of care and value generated by care tend to spend more on tools for data analytics, population health management, and health coordination. They tended to focus on clinical analytics over business intelligence, and preferred CRM adoption to telemedicine or patient portal adoption. Read the full report on how healthcare tech is moving beyond the EHR.
Truing AI Strategy by Understanding AI Risk
AI and machine learning are applied to diagnostics, prescription, risk screening, prognostic scoring, and other rule based tasks. Their future potential is far greater, and require a stronger clinical understanding of quality and safety in machine learning systems.
Whether tackling current issues, like practicing safe failure and explaining black box AI, or preparing for future ones, learn the risks you should put on your radar when thinking about an AI strategy.
Personal Health Record Tool Shows Potential at UC San Diego Health
Of 425 patient who used a personal health record at UC San Diego Health, 132 responded to a survey on their usage. Their responses praised the personal records for facilitating their communications with their clinicians and with friends and family, and for making their own health information easier to access and understand.
Does widespread smartphone usage and value-based care make personal health records and/or patient-controlled patient data a desirable target?