Chronic Diseases in High-Cost Users of Hospital, Primary Care, and Prescription Medication in the Capital Region of Denmark

Toxværd, Cecilie Goltermann; Benthien, Kirstine Skov; Andreasen, Anne Helms; Nielsen, Ann; Osler, Merete; Johansen, Nanna Borup

doi:10.1007/s11606-019-05315-w

Cited by 11 publications

(8 citation statements)

References 23 publications

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“…5 In North America and Europe, a substantial proportion of such operations are performed in patients with comorbid conditions predisposed to suffer longer hospital stays, greater healthcare expenditure and greater loss of post-surgery independence. 6,7 Adverse discharge is an outcome pertinent to this high-risk group based on corresponding associations with longer hospital and postacute care stays 8,9 as well as postoperative functional dependence. [9][10][11] Understanding risk of this outcome would aid in advanced care planning and shared decision making between patients and providers.…”

Section: Introductionmentioning

confidence: 99%

“…This more commonly encountered outcome (up to 40% in high‐risk cohorts 4 ) is devastating for previously home‐dwelling patients, who comprise the majority of the more than 170 million patients undergoing inpatient surgery in the globally each year 5 . In North America and Europe, a substantial proportion of such operations are performed in patients with comorbid conditions predisposed to suffer longer hospital stays, greater healthcare expenditure and greater loss of post‐surgery independence 6,7 …”

Section: Introductionmentioning

confidence: 99%

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Discharge Prediction for Patients Undergoing Inpatient Surgery: Development and validation of the DEPENDENSE score

Hammer

Althoff

Platzbecker

et al. 2021

Acta Anaesthesiol Scand

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BackgroundA substantial proportion of patients undergoing inpatient surgery each year is at risk for postoperative institutionalization and loss of independence. Reliable individualized preoperative prediction of adverse discharge can facilitate advanced care planning and shared decision making.MethodsUsing hospital registry data from previously home‐dwelling adults undergoing inpatient surgery, we retrospectively developed and externally validated a score predicting adverse discharge. Multivariable logistic regression analysis and bootstrapping were used to develop the score. Adverse discharge was defined as in‐hospital mortality or discharge to a skilled nursing facility. The model was subsequently externally validated in a cohort of patients from an independent hospital.ResultsIn total, 106 164 patients in the development cohort and 92 962 patients in the validation cohort were included, of which 16 624 (15.7%) and 7717 (8.3%) patients experienced adverse discharge, respectively. The model was predictive of adverse discharge with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI 0.87‐0.88) in the development cohort and an AUC of 0.86 (95% CI 0.86‐0.87) in the validation cohort.ConclusionUsing preoperatively available data, we developed and validated a prediction instrument for adverse discharge following inpatient surgery. Reliable prediction of this patient centered outcome can facilitate individualized operative planning to maximize value of care.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discharge Prediction for Patients Undergoing Inpatient Surgery: Development and validation of the DEPENDENSE score

Hammer

Althoff

Platzbecker

et al. 2021

Acta Anaesthesiol Scand

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show abstract

“…The HC population was also defined as the top 10% or top 1% of patients by medical expenditure, who are generally most affected by chronic diseases in the last years of their lives [ 4 ]. The HC population is also often associated with high need, which means that these populations often suffer from three or more chronic diseases [ 1 , 5 ]. These HC populations often require extensive attention and consume a disproportionate share of resources [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, team-based care might only provide longitudinal continuity of care rather than interpersonal continuity of care [ 21 ]. Second, the quality of these services varies widely and often cannot reach the places that need them most [ 5 ]. Third, although most studies overemphasized the utilization of acute care services, such as ED visits and hospitalization services for the HC population, nearly 70% of this spending was used in long-term care services [ 8 ], and the largest driver of inpatient spending was catastrophic events.…”

Section: Introductionmentioning

confidence: 99%

Association between service scope of primary care facilities and prevalence of high-cost population: a retrospective study in rural Guizhou, China

Ding

et al. 2022

BMC Prim. Care

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Background High-cost (HC) patients, defined as the small percentage of the population that accounts for a high proportion of health care expenditures, are a concern worldwide. Previous studies have found that the occurrence of HC population is partially preventable by providing a greater scope of primary health care services. However, no study has examined the association between the service scope of primary care facilities and the prevalence of HC populations. Therefore, this study aimed to investigate the association between the service scope of primary care facilities (PCFs) and the prevalence of HC populations within the same communities. Methods A multistage, stratified, clustered sampling method was used to identify the service scope of PCFs as of 2017 in rural Guizhou, China. The claims data of 299,633 patients were obtained from the local information system of the New Rural Cooperation Medical Scheme. Patients were sorted by per capita inpatient medical expenditures in descending order, and the top 1%, top 5% and top 10% of patients who had incurred the highest costs were defined as the HC population. Logistic regression models were used to assess the association between the service scope of PCFs and the prevalence of the HC population. Results Compared with those in the 95% of the sample deemed as the general population, those in the top 5% of the sample deemed as the HC population were more likely to be over the age of 30 (P < 0.001), to be female (P = 0.014) and to be referred to high-level hospitals (P < 0.001). After controlling for other covariates, patients who lived in the communities serviced by the PCFs with the smallest service scope were more likely to be in the top 1%, top 5% and top 10% of the HC population. Conclusion A greater PCF service scope was associated with a reduction in the prevalence of the HC population, which would mean that providing a broader PCF service scope could reduce some preventable costs, thus reducing the prevalence of the HC population. Future policy efforts should focus on expanding the service scope of primary care providers to achieve better patient outcomes.

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“…2 Persons with non-communicable diseases such as heart diseases and chronic obstructive pulmonary disease (COPD) are at risk of becoming high-cost users of healthcare resources. 3 The incidence of unplanned hospital admissions for ambulatory care sensitive conditions varies with geographical regions, bed availability and quality of primary care. 4,5 Being a high-cost user is associated with multimorbidity, mental illness, higher age and low income.…”

Section: Introductionmentioning

confidence: 99%

Predicting Individual Risk of Emergency Hospital Admissions – A Retrospective Validation Study

Benthien¹,

Jacobsen²,

Hjarnaa³

et al. 2021

RMHP

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Purpose: A high number of hospital admissions may indicate poor general health and less than optimal health care across sectors. To prevent hospital admissions, previous studies have focused on predicting readmissions relating to a defined index admission and specific condition, whereas generic models suited for community-dwelling persons are lacking. The aim of this study was to validate a generic model that predicted risk of emergency hospital admission within the following three months and to investigate regional variation. Materials and Methods: This study is an observational register-based validation study of a prediction model. The prediction model was based on a population of frail elderly, persons with non-communicable diseases, and persons with three emergency hospital admissions using information about diagnoses and hospital contacts. The prediction model consisted of two stages. In the first stage, covariate associations to admissions are estimated from observed data in one year. In the second stage, admissions are predicted in the coming three months based on observed estimations from the first stage. The validity of the model was calculated by comparing predicted and observed admissions from August 1st to October 31st, 2016. Results: The study included 112,026 persons. In nationwide data, area under the curve (AUC) was 0.7742 (95% CI 0.7698-0.7786), and the positive predictive value was 52% for the 99th percentile (the top 1%). AUC varied between regions from 0.6914 in Southern Denmark (95% CI 0.6779-0.7049) to 0.8224 (95% CI 0.8064-0.8384) in North Denmark. AUC was higher with nationwide data compared to regional. Conclusion:The model performed satisfactorily in predicting individual risk of emergency hospital admission.

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Chronic Diseases in High-Cost Users of Hospital, Primary Care, and Prescription Medication in the Capital Region of Denmark

Cited by 11 publications

References 23 publications

Discharge Prediction for Patients Undergoing Inpatient Surgery: Development and validation of the DEPENDENSE score

Discharge Prediction for Patients Undergoing Inpatient Surgery: Development and validation of the DEPENDENSE score

Association between service scope of primary care facilities and prevalence of high-cost population: a retrospective study in rural Guizhou, China

Predicting Individual Risk of Emergency Hospital Admissions – A Retrospective Validation Study

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