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The untapped potential of payer care management

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In our experience, many payers are dedicating 10 percent or more of administrative spend on care management, 1 1. Based on aggregate McKinsey client experience. Many payers have traditionally expressed more confidence in levers such as utilization management, payment integrity, or network to manage medical cost and boost revenue. The COVID-19 crisis, however, has created a need to reset the vision for payer care management to help payers respond to increased strain on member whole-person health, member concern about visiting common sites of care, and potential risk posed by COVID-19 in skilled nursing facilities. According to a recent McKinsey COVID-19 Consumer Survey, nearly 80 percent of respondents said that they have experienced distress related to COVID-19 and over 50 percent of respondents said that they have felt anxious or depressed over the past week, which may suggest a need for payers to better support whole-person health. 2 2. Cordina J, Levin E, and Ramish A, "Helping US healthcare stakeholders understand the human side of the COVID-19 crisis: McKinsey Consumer Healthcare Insights," September 18, 2020, McKinsey.com. Additionally, while members are increasingly more comfortable returning to traditional sites of care, some are still not, which may require payers to support care at home for members who feel uncomfortable visiting facilities.


Data analytics aids value-based reimbursement, but bigger goals loom

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Healthcare organizations have spent years installing electronic health records and other information systems that collect data and improve patient care. Is the information collected by Fitbits and Apple Watches covered by HIPAA regulations? Find out more about what's covered โ€“ and what isn't โ€“ when it comes to wearable devices and data so you can avoid the risks. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent.


Healthcare Analytics: Analytics and machine learning - Analytics Magazine

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The U.S. healthcare system is well on its way in the transition to value-based payment models that reward providers for delivering quality outcomes and keeping patients healthy. In fact, as of March 2016, the Department of Health and Human Services reported that an estimated 30 percent of Medicare payments were already tied to these new alternative payment systems. Value-based programs are replacing traditional fee-for-service models that pay providers based on the number of services delivered. The newer models are designed to encourage care that is well-coordinated, cost-effective and lead to quality patient outcomes. In order to achieve new payment objectives, providers are seeking opportunities to engage patients in their own care, improve patient satisfaction and keep patients healthier.


Can AI Transform Patient Care from Reactive Craft to Strategic Art? -

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Personalized Analytics is becoming essential in healthcare, stemming from the movement from fee-for-service to a value-based market. The need to preempt and prevent disease on a more personal level, rather than merely reacting to symptoms, has created a significant opportunity for machine learning-based applications. This "analytics of one" approach (using advanced mathematical models and artificial intelligence techniques) is already impacting several key areas: Prime examples include cardiac imaging analysis that aides physicians in assessing conditions, including heart attacks and coronary artery disease, and retinal image analysis to detect diabetic retinopathy. The anticipated goal for AI in healthcare is to enhance and expand the "four Ps" of care delivery โ€“ predictive, preventative, personalized and participatory. Predictive: Predictions have existed in healthcare for some decades now, as statistical models based on structured data sources.


A Deep Learning Model for Pediatric Patient Risk Stratification

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Artificial intelligence based on medical claims data outperforms traditional models in stratifying patient risk. ABSTRACT Objectives: Current models for patient risk prediction rely on practitioner expertise and domain knowledge. This study presents a deep learning model--a type of machine learning that does not require human inputs--to analyze complex clinical and financial data for population risk stratification. Methods: "Skip-Gram," an unsupervised deep learning approach that uses neural networks for prediction modeling, used data from 2014 and 2015 to predict the risk of hospitalization in 2016. The area under the curve (AUC) of the deep learning model was compared with that of both the Clinical Classifications Software and the commercial DxCG Intelligence predictive risk models, each with and without demographic and utilization features.