scholarly journals Bridging the impactibility gap in population health management: a systematic review

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e052455
Author(s):  
Andi Orlowski ◽  
Sally Snow ◽  
Heather Humphreys ◽  
Wayne Smith ◽  
Rebecca Siân Jones ◽  
...  
Keyword(s):  
Systematic Review ◽  
Risk Stratification ◽  
Population Health ◽  
Health Management ◽  
Care Quality ◽  
Health Conditions ◽  
Clinical Judgement ◽  
Qualitative Synthesis ◽  
Population Health Management ◽  
Hospitalised Patients

ObjectivesAssess whether impactibility modelling is being used to refine risk stratification for preventive health interventions.DesignSystematic review.SettingPrimary and secondary healthcare populations.PapersArticles published from 2010 to 2020 on the use or implementation of impactibility modelling in population health management, reported with the terms ‘intervenability’, ‘amenability’, and ‘propensity to succeed’ (PTS) and associated with the themes ‘care sensitivity’, ‘characteristic responders’, ‘needs gap’, ‘case finding’, ‘patient selection’ and ‘risk stratification’.InterventionsQualitative synthesis to identify themes for approaches to impactibility modelling.ResultsOf 1244 records identified, 20 were eligible for inclusion. Identified themes were ‘health conditions amenable to care’ (n=6), ‘PTS modelling’ (n=8) and ‘comparison or combination with clinical judgement’ (n=6). For the theme ‘health conditions amenable to care’, changes in practice did not reduce admissions, particularly for ambulatory care sensitive conditions, and sometimes increased them, with implementation noted as a possible issue. For ‘PTS modelling’, high costs and needs did not necessarily equate to high impactibility and targeting a larger number of individuals with disorders associated with lower costs had more potential. PTS modelling seemed to improve accuracy in care planning, estimation of cost savings, engagement and/or care quality. The ‘comparison or combination with clinical judgement’ theme suggested that models can reach reasonable to good discriminatory power to detect impactable patients. For instance, a model used to identify patients appropriate for proactive multimorbid care management showed good concordance with physicians (c-statistic 0.75). Another model employing electronic health record scores reached 65% concordance with nurse and physician decisions when referring elderly hospitalised patients to a readmission prevention programme. However, healthcare professionals consider much wider information that might improve or impede the likelihood of treatment impact, suggesting that complementary use of models might be optimum.ConclusionsThe efficiency and equity of targeted preventive care guided by risk stratification could be augmented and personalised by impactibility modelling.

scholarly journals Informatics strategies for risk stratification in population health management

2017 ◽  
Author(s):  
◽  
Lincoln Sheets
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Population Health ◽  
Healthcare Costs ◽  
Health Management ◽  
Area Under The Curve ◽  
Population Health Management ◽  
Interpretable Machine Learning ◽  
Risk Patients ◽  
Improve Health

Risk analysis and population health management can improve health outcomes, but improved risk stratification is needed to manage healthcare costs. Analysis of 157 publications on translational implementations of "risk stratification in population health management of chronic disease" showed a consensus that population health management and risk stratification can improve outcomes, but found uncertainty over best methods for risk prediction and controversy over the cost savings. The consensus of another 85 publications on the methodologies of "data mining for predictive healthcare analytics" was that clinically interpretable machine learning techniques are more appropriate than "black box" techniques for structured big data sources in healthcare, and the "area under the curve" of a prediction model's sensitivity versus one-minus-specificity is a standard and reliable way to measure the model's discrimination. This study used clinically interpretable machine-learning algorithms, combined with simple but powerful data analytic techniques such as cost analysis and data visualization, to evaluate and improve risk stratification for a managed patient population. This study retrospectively observed 10,000 mid-Missouri Medicare and Medicaid patients between 2012 and 2014. Cost and utilization analyses, statistical clustering, contrast mining, and logistic regression were used to identify patients within a managed population at risk for higher healthcare costs, demonstrate longitudinal changes in risk stratification, and characterize detailed differences between high-risk and low-risk patients. The two highest risk stratification tiers comprised only 21% of patients but accounted for 43% of prospective charges. Patients in the most expensive sub-cluster of the most expensive risk tier were nearly twice as costly as high-risk patients on average. Combining contrast mining with logistic regression predicted the most expensive 5% of patients with 84% accuracy, as measured by area under the curve. All the strategies used in this study, from the simplest to the most sophisticated, produced useful insights. By predicting the small number of patients who will incur the majority of healthcare expenses in terms that are clinically interpretable, these methods can support population health managers in focusing preventive and longitudinal care more effectively. These models, and similar models developed by integrating diverse informatics strategies, could improve health outcomes, delivery, and costs.

Accountable Care: Building Systems for Population Health Management

2014 ◽  
Author(s):  
Sarah Klein Klein ◽  
Douglas McCarthy McCarthy ◽  
Alexander Cohen Cohen
Keyword(s):  
Population Health ◽  
Health Management ◽  
Accountable Care ◽  
Population Health Management ◽  
Building Systems

Using High-Tech and High-Touch Methods for Effective Population Health Management

Iproceedings ◽  
2016 ◽  
Vol 2 (1) ◽  
pp. e17
Author(s):  
Sashi Padarthy ◽  
Cristina Crespo ◽  
Keri Rich ◽  
Nagaraja Srivatsan
Keyword(s):  
Population Health ◽  
Health Management ◽  
High Tech ◽  
Effective Population ◽  
Population Health Management

scholarly journals Reasons why people do not attend NHS Health Checks: a systematic review and qualitative synthesis

2017 ◽  
Vol 68 (666) ◽  
pp. e28-e35 ◽  
Author(s):  
Emma Harte ◽  
Calum MacLure ◽  
Adam Martin ◽  
Catherine L Saunders ◽  
Catherine Meads ◽  
...  
Keyword(s):  
Systematic Review ◽  
Health Management ◽  
Health Check ◽  
Cochrane Library ◽  
Inclusion Criteria ◽  
Qualitative Synthesis ◽  
Health Checks ◽  
The Impact ◽  
Nhs Health Checks ◽  
Nhs Health Check

BackgroundThe NHS Health Check programme is a prevention initiative offering cardiovascular risk assessment and management advice to adults aged 40–74 years across England. Its effectiveness depends on uptake. When it was introduced in 2009, it was anticipated that all those eligible would be invited over a 5-year cycle and 75% of those invited would attend. So far in the current cycle from 2013 to 2018, 33.8% of those eligible have attended, which is equal to 48.5% of those invited to attend. Understanding the reasons why some people do not attend is important to maximise the impact of the programmes.AimTo review why people do not attend NHS Health Checks.Design and settingA systematic review and thematic synthesis of qualitative studies.MethodAn electronic literature search was carried out of MEDLINE, Embase, Health Management Information Consortium, Cumulative Index to Nursing and Allied Health Literature, Global Health, PsycINFO, Web of Science, OpenGrey, the Cochrane Library, NHS Evidence, Google Scholar, Google, ClinicalTrials.gov, and the ISRCTN registry from 1 January 1996 to 9 November 2016, and the reference lists of all included papers were also screened manually. Inclusion criteria were primary research studies that reported the views of people who were eligible for but had not attended an NHS Health Check.ResultsNine studies met the inclusion criteria. Reasons for not attending included lack of awareness or knowledge, misunderstanding the purpose of the NHS Health Check, aversion to preventive medicine, time constraints, difficulties with access to general practices, and doubts regarding pharmacies as appropriate settings.ConclusionThe findings particularly highlight the need for improved communication and publicity around the purpose of the NHS Health Check programme and the personal health benefits of risk factor detection.

Technology-Enabled Population Health Management: Two Communities' Use of an Electronic Care Alert System

PM&R ◽  
2017 ◽  
Vol 9 ◽  
pp. S75-S84 ◽  
Author(s):  
Todd Rowland ◽  
Jill Nielsen-Farrell ◽  
Kathy Church ◽  
Barbara Riddell
Keyword(s):  
Population Health ◽  
Health Management ◽  
Alert System ◽  
Population Health Management
Sign in / Sign up

Export Citation Format

Share Document