Evolving Perspectives on Population Health Management

James, Paul A.; Kuzel, Anton J.; Thompson, Barbara; Davis, Ardis; Grumbach, Kevin

doi:10.1370/afm.1702

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…9 The purpose of health management is from "disease intervention" to "disease prevention" and from "treatment of symptoms but not the cause of disease" to "treatment of both symptoms and cause". 10 Since the concept of health management was introduced into China at the end of the 20th century, it has been developed obviously in theoretical education and industrial practice. 11 To explore a new health management mode, the National Stroke Prevention and Treatment Engineering Committee launched the cultivation program for stroke health managers (renamed as brain and heart health managers in 2018) in 2017, aiming to provide pre-hospital, inhospital and post-hospital health management services for people at high risk of stroke (including patients with a history of stroke) with hospital support.…”

Section: Introductionmentioning

confidence: 99%

Can Goal-Based Health Management Improve the Health Knowledge, Health Belief and Health Behavior in People at High Risk of Stroke? A Non-Randomized Controlled Trial

Guo

Liu

et al. 2021

NDT

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Purpose: To examine the effectiveness of goal-based health management on health knowledge, health belief and health behavior in people at high risk of stroke. Patients and Methods: This is a two-armed and non-randomized controlled trial from May 31, 2020 to December 31, 2020. Participants were arranged to receive regular health care (n=126) or goal-based health management (n=126). The generalized estimation equation was used to analyze the changes in the participants' health knowledge, health belief and health behavior at baseline (T0), 1-month (T1), 3-month (T3), and 6-month (T6). Results: A total of 243 participants completed the 6-month study, of which 123 were in the control group and 120 were in the intervention group. After receiving goal-based health management, participants' health behavior (β=27.154, p<0.001), health knowledge (β=10.504, p<0.001) and health belief (β=49.057, p<0.001) scores increased significantly. Conclusion:Goal-based health management can effectively improve the health knowledge, health belief and health behavior of people at high risk of stroke. We recommend that nurses can provide goal-based health management for people at high risk of stroke with support from hospitals and family members of people at high risk of stroke.

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Section: Introductionmentioning

confidence: 99%

Can Goal-Based Health Management Improve the Health Knowledge, Health Belief and Health Behavior in People at High Risk of Stroke? A Non-Randomized Controlled Trial

Guo

Liu

et al. 2021

NDT

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“…This is an important PHM initiative that the RHS database has addressed. Improving care transitions, or “handoffs” as patients move from one care setting to another, is a priority across stakeholder groups and RHSs (Marchibroda, ; James et al ., ; Marcotte et al ., ), and the RHS database aims to facilitate this seamless transition.…”

Section: Discussionmentioning

confidence: 99%

Singapore's regional health systems-a data-driven perspective on frequent admitters and cross utilization of healthcare services in three systems

Saxena

You

Zhu

et al. 2015

Int J Health Plann Mgmt

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“…During the last decade, the use of health management programs to help people live healthier lives has increased, especially for those with multiple chronic conditions [1]. A good population health management approach takes into account the multiple determinants of health, including the adequacy of medical care, public health interventions, social and physical environments and related services, genetics, and individual behavior [2].…”

Section: Introductionmentioning

confidence: 99%

Leveraging big data in population health management

et al. 2016

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Background: Population health management takes into account many determinants of health, including medical care, social and physical environments and related services, genetics, and individual behavior. Many different types of data may be used to guide population health management programs and to estimate program value. In addition to the variety of data required for these programs, big population health program data are characterized by large volume, high velocity, and inconsistent data flows. This manuscript describes how big data analytics have been used to craft a population health program to help improve the lives of about four million older adults who have an AARP® Medicare Supplement Insurance plan (i.e., a Medigap plan). Plan enrollees have access to a wellness program, holistic care coordination programs, two telephone-based advice lines, concierge support for insurance and medical care needs, and a program designed to help reduce unnecessary emergency room (ER) visits. Results: During 2009-2011, these program components led to several improvements in health care. For example, increased duration in care coordination was associated with fewer hospital readmissions, and participants were significantly more likely to have recurring physician office visits and recommended laboratory tests. Participants in ER decision support reduced their ER visits by 1299 visits per 1000 insureds, compared with a reduction of 1121 visits per 1000 insureds for individuals who did not participate in the program. Better depression management helped reduce depression symptoms in 59 % of participants engaged in that program. Big data analytics of member data suggested the need for a wellness program feature, which began in 2014. Analytics of disease management services offered in 2009-2011 helped to combine and refocus these program features to enhance effectiveness in later years. Conclusions: Using big data to help manage and evaluate a population health program has led to several improvements in health care. Program management, reporting, and evaluation processes generated additional data which, when analyzed, continues to refine program implementation and quality. Future improvements to this program may include enhanced integration of social service programs that will generate their own data streams for analyses designed to further improve health and wellbeing.

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Evolving Perspectives on Population Health Management

Cited by 3 publications

References 6 publications

Can Goal-Based Health Management Improve the Health Knowledge, Health Belief and Health Behavior in People at High Risk of Stroke? A Non-Randomized Controlled Trial

Can Goal-Based Health Management Improve the Health Knowledge, Health Belief and Health Behavior in People at High Risk of Stroke? A Non-Randomized Controlled Trial

Singapore's regional health systems-a data-driven perspective on frequent admitters and cross utilization of healthcare services in three systems

Leveraging big data in population health management

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