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Predictive ability of an expert-defined population segmentation framework for healthcare utilization and mortality - a retrospective cohort study.

ABSTRACT: BACKGROUND:Population segmentation of patients into parsimonious and relatively homogenous subgroups or segments based on healthcare requirements can aid healthcare resource planning and the development of targeted intervention programs. In this study, we evaluated the predictive ability of a previously described expert-defined segmentation approach on 3-year hospital utilization and mortality. METHODS:We segmented all adult patients who had a healthcare encounter with Singapore Health Services (SingHealth) in 2012 using the SingHealth Electronic Health Records (SingHealth EHRs). Patients were divided into non-overlapping segments defined as Mostly Healthy, Stable Chronic, Serious Acute, Complex Chronic without Frequent Hospital Admissions, Complex Chronic with Frequent Hospital Admissions, and End of Life, using a previously described expert-defined segmentation approach. Hospital admissions, emergency department attendances (ED), specialist outpatient clinic attendances (SOC) and mortality in different patient subgroups were analyzed from 2013 to 2015. RESULTS:819,993 patients were included in this study. Patients in Complex Chronic with Frequent Hospital Admissions segment were most likely to have a hospital admission (IRR 22.7; p

SUBMITTER: Low LL

PROVIDER: S-EPMC6585096 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Predictive ability of an expert-defined population segmentation framework for healthcare utilization and mortality - a retrospective cohort study.

Low Lian Leng LL Kwan Yu Heng YH Ma Cheryl Ann CA Yan Shi S Chia Elian Hui San EHS Thumboo Julian J

BMC health services research 20190620 1

<h4>Background</h4>Population segmentation of patients into parsimonious and relatively homogenous subgroups or segments based on healthcare requirements can aid healthcare resource planning and the development of targeted intervention programs. In this study, we evaluated the predictive ability of a previously described expert-defined segmentation approach on 3-year hospital utilization and mortality.<h4>Methods</h4>We segmented all adult patients who had a healthcare encounter with Singapore H ...[more]

PMID: 31221139

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Project description:BackgroundSegmentation of heterogeneous patient populations into parsimonious and relatively homogenous groups with similar healthcare needs can facilitate healthcare resource planning and development of effective integrated healthcare interventions for each segment. We aimed to apply a data-driven, healthcare utilization-based clustering analysis to segment a regional health system patient population and validate its discriminative ability on 4-year longitudinal healthcare utilization and mortality data.MethodsWe extracted data from the Singapore Health Services Electronic Health Intelligence System, an electronic medical record database that included healthcare utilization (inpatient admissions, specialist outpatient clinic visits, emergency department visits, and primary care clinic visits), mortality, diseases, and demographics for all adult Singapore residents who resided in and had a healthcare encounter with our regional health system in 2012. Hierarchical clustering analysis (Ward's linkage) and K-means cluster analysis using age and healthcare utilization data in 2012 were applied to segment the selected population. These segments were compared using their demographics (other than age) and morbidities in 2012, and longitudinal healthcare utilization and mortality from 2013-2016.ResultsAmong 146,999 subjects, five distinct patient segments "Young, healthy"; "Middle age, healthy"; "Stable, chronic disease"; "Complicated chronic disease" and "Frequent admitters" were identified. Healthcare utilization patterns in 2012, morbidity patterns and demographics differed significantly across all segments. The "Frequent admitters" segment had the smallest number of patients (1.79% of the population) but consumed 69% of inpatient admissions, 77% of specialist outpatient visits, 54% of emergency department visits, and 23% of primary care clinic visits in 2012. 11.5% and 31.2% of this segment has end stage renal failure and malignancy respectively. The validity of cluster-analysis derived segments is supported by discriminative ability for longitudinal healthcare utilization and mortality from 2013-2016. Incident rate ratios for healthcare utilization and Cox hazards ratio for mortality increased as patient segments increased in complexity. Patients in the "Frequent admitters" segment accounted for a disproportionate healthcare utilization and 8.16 times higher mortality rate.ConclusionOur data-driven clustering analysis on a general patient population in Singapore identified five patient segments with distinct longitudinal healthcare utilization patterns and mortality risk to provide an evidence-based segmentation of a regional health system's healthcare needs.

Project description:BackgroundHeterogeneity of population health needs and the resultant difficulty in health care resources planning are challenges faced by primary care systems globally. To address this challenge in population health management, it is critical to have a better understanding of primary care utilizers' heterogeneous health profiles. We aimed to segment a population of primary care utilizers into classes with unique disease patterns, and to report the 1 year follow up healthcare utilizations and all-cause mortality across the classes.MethodsUsing de-identified administrative data, we included all adult Singapore citizens or permanent residents who utilized Singapore Health Services (SingHealth) primary care services in 2012. Latent class analysis was used to identify patient subgroups having unique disease patterns in the population. The models were assessed by Bayesian Information Criterion and clinical interpretability. We compared healthcare utilizations in 2013 and one-year all-cause mortality across classes and performed regression analysis to assess predictive ability of class membership on healthcare utilizations and mortality.ResultsWe included 100,747 patients in total. The best model (k = 6) revealed the following classes of patients: Class 1 "Relatively healthy" (n = 58,213), Class 2 "Stable metabolic disease" (n = 26,309), Class 3 "Metabolic disease with vascular complications" (n = 2964), Class 4 "High respiratory disease burden" (n = 1104), Class 5 "High metabolic disease without complication" (n = 11,122), and Class 6 "Metabolic disease with multi-organ complication" (n = 1035). The six derived classes had different disease patterns in 2012 and 1 year follow up healthcare utilizations and mortality in 2013. "Metabolic disease with multiple organ complications" class had the highest healthcare utilization (e.g. incidence rate ratio = 19.68 for hospital admissions) and highest one-year all-cause mortality (hazard ratio = 27.97).ConclusionsPrimary care utilizers are heterogeneous and can be segmented by latent class analysis into classes with unique disease patterns, healthcare utilizations and all-cause mortality. This information is critical to population level health resource planning and population health policy formulation.

Project description:Background Canadian Cardiovascular Society (CCS) angina severity classification is associated with mortality, myocardial infarction, and coronary revascularization in clinical trial and registry data. The objective of this study was to determine associations between CCS class and all-cause mortality and healthcare utilization, using natural language processing to extract CCS classifications from clinical notes. Methods and Results In this retrospective cohort study of veterans in the United States with stable angina from January 1, 2006, to December 31, 2013, natural language processing extracted CCS classifications. Veterans with a prior diagnosis of coronary artery disease were excluded. Outcomes included all-cause mortality (primary), all-cause and cardiovascular-specific hospitalizations, coronary revascularization, and 1-year healthcare costs. Of 299 577 veterans identified, 14 216 (4.7%) had ≥1 CCS classification extracted by natural language processing. The mean age was 66.6±9.8 years, 99% of participants were male, and 81% were white. During a median follow-up of 3.4 years, all-cause mortality rates were 4.58, 4.60, 6.22, and 6.83 per 100 person-years for CCS classes I, II, III, and IV, respectively. Multivariable adjusted hazard ratios for all-cause mortality comparing CCS II, III, and IV with those in class I were 1.05 (95% CI, 0.95-1.15), 1.33 (95% CI, 1.20-1.47), and 1.48 (95% CI, 1.25-1.76), respectively. The multivariable hazard ratio comparing CCS IV with CCS I was 1.20 (95% CI, 1.09-1.33) for all-cause hospitalization, 1.25 (95% CI, 0.96-1.64) for acute coronary syndrome hospitalizations, 1.00 (95% CI, 0.80-1.26) for heart failure hospitalizations, 1.05 (95% CI, 0.88-1.25) for atrial fibrillation hospitalizations, 1.92 (95% CI, 1.40-2.64) for percutaneous coronary intervention, and 2.51 (95% CI, 1.99-3.16) for coronary artery bypass grafting surgery. Conclusions Natural language processing-extracted CCS classification was positively associated with all-cause mortality and healthcare utilization, demonstrating the prognostic importance of anginal symptom assessment and documentation.

Project description:ObjectivesEnsuring access to care for all patients-especially those with life-threatening and chronic conditions-during a pandemic is a challenge for all healthcare systems. During the COVID-19 pandemic, many countries faced excess mortality partly attributed to disruptions in essential healthcare services provision. This study aims to estimate the utilization of public primary care and hospital services during the COVID-19 epidemic in Greece and its potential association with excess non-COVID-19 mortality in the country.Study designThis is an observational study.MethodsA retrospective analysis of national secondary utilization and mortality data from multiple official sources, covering the first nine months of the COVID-19 epidemic in Greece (February 26th to November 30th, 2020), was carried out.ResultsUtilization rates of all public healthcare services during the first nine months of the epidemic dropped significantly compared to the average utilization rates of the 2017-19 control period; hospital admissions, hospital surgical procedures, and primary care visits dropped by 17.3% (95% CI: 6.6%-28.0%), 23.1% (95% CI: 7.3%-38.9%), and 24.8% (95% CI: 13.3%-36.3%) respectively. This underutilization of essential public services-mainly due to supply restrictions such as suspension of outpatient care and cancelation of elective surgeries-is most probably related to the 3778 excess non-COVID-19 deaths (representing 62% of all-cause excess deaths) that have been reported during the first 9 months of the epidemic in the country.ConclusionsGreece's healthcare system, deeply wounded by the 2008-18 recession and austerity, was ill-resourced to cope with the challenges of the COVID-19 epidemic. Early and prolonged lockdowns have kept COVID-19 infections and deaths at relatively low levels. However, this "success" seems to have been accomplished at the expense of non-COVID-19 patients. It is important to acknowledge the "hidden epidemic" of unmet non-COVID-19 needs and increased non-COVID-19 deaths in the country and urgently strengthen public healthcare services to address it.

Project description:BackgroundCOPD is a significant cause of morbidity and mortality in the Veterans Health Administration (VHA). To determine the clinical factors associated with the cost of COPD management, we analyzed the relationship between clinical characteristics and COPD healthcare costs at the Cincinnati VAMC.MethodsWe queried the VHA Decision Support System for patients diagnosed with COPD at the Cincinnati VAMC and calculated their VHA COPD-related encounters and costs in FY2008. Patients were ranked by COPD-related cost. We determined the detailed clinical characteristics of patients selected by modified systematic sampling and performed univariate and multivariable ordinary linear regression analysis to determine factors associated with cost.Results3263 Veterans had 11,869 encounters with a primary or secondary diagnosis of COPD: 10,032 clinic visits, 505 emergency department (ED) visits, and 1,332 hospitalizations and incurred a total COPD-related healthcare cost of $21.4 M: $2.4 M clinic visits, $0.21 M ED visits, and $18.7 M hospitalizations and $0.89 M for COPD-related prescription costs. When the patients were ranked by VHA healthcare costs, the top 20% of patients accounted for 86% of the total costs and 57% of the total encounters with a primary or secondary diagnosis code of COPD and 90% of the total costs and 75% of the total encounters with a primary diagnosis code of COPD. The clinical characteristics and VHA healthcare costs of 840 of the 3263 unique individuals with COPD were analyzed to determine those characteristics associated with increased COPD-related costs. Univariate analysis showed significant associations with 24 clinical variables; the 4 most highly associated factors were nursing home residence, total hospital admissions, use of oral corticosteroids, and supplemental oxygen (p < 0.001 for all). In multivariate analysis, total number of admissions (p < 0.001), management by a pulmonologist (p < 0.001), number of clinic visits (p < 0.001), use of short acting anticholinergic (p = 0.001), forced expiratory volume in 1 second (FEV1) (p = 0.011), number of prescriptions (p = 0.011), body mass index (BMI) (p = 0.025), and use of inhaled corticosteroid (p = 0.043) were associated with COPD management cost.ConclusionThe total number of admissions, clinic visits, physiologic impairment, BMI, number of medications, and type of provider are strongly associated with the total cost of COPD management. These factors may be used to focus COPD management toward patients with the potential for high utilization of healthcare resources.

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Predictive ability of an expert-defined population segmentation framework for healthcare utilization and mortality - a retrospective cohort study.

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Predictive ability of an expert-defined population segmentation framework for healthcare utilization and mortality - a retrospective cohort study.

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