Healthcare presents a unique case for DX because of its complex and challenging nature. Nowhere is this precedent more applicable than for the United States, where regulatory frameworks (e.g., MACRA), incentive programs (e.g., QPP, MIPS, and APMs), professional and community advocacy, and consumer-driven market forces are shifting healthcare priorities. These factors are driving the need for data-driven decisions and consumer engagement to recalibrate care from the mere fulfillment of fee for service and driving volume to the realization of pay for performance and driving value. U.S. healthcare organizations have much to gain by embracing DX on their journey toward value-based goals and responding to future challenges.
We hear everyday of the challenges facing provider organizations as they straddle the shifting reimbursement landscape. Well grounded in the fee for service reimbursement models, providers struggle to balance their volume-based operating models with the growth of value-based models. In the former model, if a procedure is done it is reimbursed, regardless of the outcome. The latter it is the outcome that is rewarded and for which at least partial payment is attributed. Adding additional financial risk is the requirement for providers to invest in technology and people to deliver the results required of the contract while not understanding what an expected return on investment might be. We have long understood that improved quality eventually translates into savings, but what are providers to do in the short term to keep the lights on?
Patient engagement is a journey that offers an advantageous paradigm for healthcare digital transformation strategies. Conceptually, it builds on an appreciation of the changing role for patients (and their families) in modern day healthcare and the importance of generating efforts that organize towards engaging them as care-seeking consumers. In application, patient engagement and its underlying technologies can transform care-seeking consumers into new patients and empower existing patients to become more involved in their health and care. With time this journey can culminate into a digital transformation end state, which combines a mix of patient engagement technologies that collectively achieve an inimitable, competitive, and highly necessary advantage for the provider organization that is conducive to success.
IDC Health Insights recently published two DecisionScape reports on patient engagement, IDC TechScape: U.S. Healthcare Provider Patient Engagement Technologies, 2017 and IDC PlanScape: Patient Engagement for Digital Transformation. The TechScape supplies insights into the risks and business impact of patient engagement technologies to allow provider organizations to better match the technologies to their relative appetite for risk and make informed decisions regarding them. The PlanScape provides a decision-making tool to justify strategic investments and opportunities for providers implementing patient engagement technologies as part of digital transformation strategies. These two reports work in tandem, as the TechScape offers a visual representation of patient engagement technology adoption based on their assessment and characterization into transformational, incremental, or opportunistic technologies; while the PlanScape outlines the who, what, why, and how of patient engagement for digital transformation.
IDC Health Insights recently published IDC Survey: Provider Investment Plans for Robotics. This survey presents key findings from the healthcare section of IDC’s 2017 Worldwide Robotics Survey. The goal of the survey was to assess current and future robotics adoption patterns, use cases, and application areas, as well as investment trends for robots and drones. The results focused on robotics adoption, use cases, and investment plans by U.S. healthcare providers at hospitals with 200+ beds. While the adoption of drones for healthcare utilization is minimal and expected to remain so in the near future, the use of robots in healthcare will yield increased investment in the coming years.
Embedding machine learning in healthcare is slowly moving into mainstream. Still a very noisy market with what seems like 100s of start-ups. Through the noise emerges vendors that at early stage are applying machine learning to solve some of healthcare's most pressing problems. The use case getting the most traction in the market is the application of machine learning to predictive analytics, particularly to identify patient's with clinical and financial risk. Other applications include automating medical record review to validate Hierarchical Condition Coding (HCC), a process that was manual and caused friction between payers and providers, improving patient engagement for care management through mobile technology and identifying variation in clinical practice and recommending best practices.