A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods-Reference-Cited by-同舟云学术

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A Framework for Predicting Impactability of Digital Care Management Using Machine Learning Methods

Published:2020-08-01 Issue:4 Volume:23 Page:319-325
ISSN:1942-7891
Container-title:Population Health Management
language:en
Short-container-title:Population Health Management

Author:

Mattie Heather¹²,Reidy Patrick²,Bachtiger Patrik¹,Lindemer Emily²,Nikolaev Nikolay²,Jouni Mohammad²,Schaefer Joann³,Sherman Michael⁴,Panch Trishan¹²

Affiliation:

1. Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

2. Wellframe, Inc., Boston, Massachusetts, USA.

3. BlueCross BlueShield Nebraska, Omaha, Nebraska, USA.

4. Harvard Pilgrim Health Care, Wellesley, Massachusetts, USA.

Publisher

Mary Ann Liebert Inc

Subject

Public Health, Environmental and Occupational Health,Health Policy,Leadership and Management

Link

https://www.liebertpub.com/doi/pdf/10.1089/pop.2019.0132

Reference24 articles.

1. Artificial intelligence, machine learning and health systems

2. Integrated Telehealth And Care Management Program For Medicare Beneficiaries With Chronic Disease Linked To Savings

3. Focusing on High-Cost Patients — The Key to Addressing High Costs?

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1. Exploration of understanding of impactibility analysis and application through workshops to inform model design for population health management policy: a qualitative assessment;BMJ Open;2024-05

2. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2—Data From Nonwearables;Value in Health;2022-12

3. A Critical Review of Global Digital Divide and the Role of Technology in Healthcare;Cureus;2022-09-29

4. Predictive Modeling for Complex Care Management;2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2021-12-09

5. Bridging the impactibility gap in population health management: a systematic review;BMJ Open;2021-12

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