Dataset Information

Predicting avoidable hospital events in Maryland.

ABSTRACT:

Objective

To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland.

Data sources

Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets.

Study design

Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scores was gauged using a concentration curve.

Data collection/extraction methods

One hundred and ninety-eight individual- and ZIP code-level risk factors were used to create an analytic person-month data set of over 11.6 million person-month observations.

Principal findings

We included 198 risk factors for the model based on the results of a targeted literature review, both at the individual and neighborhood levels. These risk factors span six domains as follows: diagnoses, pharmacy utilization, procedure history, prior utilization, social determinants of health, and demographic information. Feature selection retained 73 highly statistically significant risk factors (p < 0.0012) in the primary model. Risk scores were estimated for each individual in the cohort, and, for scores released in April 2020, the top 10% riskiest individuals in the cohort account for 48.7% of avoidable hospital events in the following month. These scores significantly outperform the Centers for Medicare & Medicaid Services hierarchical condition category risk scores in terms of predictive power.

Conclusions

A risk prediction model based on standard administrative claims data can identify individuals at risk of incurring a future avoidable hospital event with good accuracy.

SUBMITTER: Henderson M

PROVIDER: S-EPMC8763284 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

Predicting avoidable hospital events in Maryland.

Henderson Morgan M Han Fei F Perman Chad C Haft Howard H Stockwell Ian I

Health services research 20211028 1

<h4>Objective</h4>To develop and validate a prediction model of avoidable hospital events among Medicare fee-for-service (FFS) beneficiaries in Maryland.<h4>Data sources</h4>Medicare FFS claims from Maryland from 2017 to 2020 and other publicly available ZIP code-level data sets.<h4>Study design</h4>Multivariable logistic regression models were used to estimate the relationship between a variety of risk factors and future avoidable hospital events. The predictive power of the resulting risk scor ...[more]

PMID: 34648179

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Project description:IntroductionAdverse healthcare-related events (AE) entail reduced patient safety. Estimating their frequency, characteristics, avoidability and impact is a means to identify targets for improvement in the quality of care.MethodsThis was a descriptive observational study conducted within the Patient Safety Incident Study in Hospitals in the Community of Madrid (ESHMAD). The study was conducted in a high-complexity hospital in May 2019 through a two-phase electronic medical record review: (1) AE screening and epidemiological and clinical data collection and (2) AE review and classification and analysis of their impact, avoidability, and associated costs.ResultsA total of 636 patients were studied. The prevalence of AE was 12.4%. Death during the stay was associated with the presence of AE (OR [CI95%]: 2.15 [1.07 to 4.52]) versus absence and emergency admission (OR [CI95%]: 17.11[6.63 to 46.26]) versus scheduled. A total of 70.2% of the AEs were avoidable. Avoidable AEs were associated with the presence of pressure ulcers (OR [CI95%]: 2.77 [1.39 to 5.51]), central venous catheter (OR [CI95%]: 2.58 [1.33 to 5.00]) and impaired mobility (OR [CI95%]: 2.24[1.35 to 3.71]), versus absences. They were associated too with the stays in the intensive care unit (OR [CI95%]: 2.75 [1.07 to 7.06]) versus medical service. AEs were responsible for additional costs of €909,716.8 for extra days of stay and €12,461.9 per patient with AE.ConclusionsThe prevalence of AEs was similar to that found in other studies. AEs led to worse patient outcomes and were associated with the patient's death. Although avoidable AEs were less severe, their higher frequency produced a greater impact on the patient and healthcare system.Key messagesAdverse events are one of the main problems in healthcare delivery and patients who suffer from at least one AE are double as likely to die during hospitalization.Avoidable adverse events are the most frequent in health care and they are a good target where achieve improvement areas that allow getting optimal patient safety and quality of care levels.Patients hospitalized in the ICU, with the previous presence of pressure ulcers, central venous catheter, or impaired mobility were associated with the development of avoidable AE, so optimal management of these patients would reduce the impact of AE.

Project description:Background and objectivesPrior approaches to identifying potentially avoidable hospital transfers (PAHs) of nursing home residents have involved detailed root cause analyses that are difficult to implement and sustain due to time and resource constraints. They relied on the presence of certain conditions but did not identify the specific issues that contributed to avoidability. We developed and tested an instrument that can be implemented using review of the electronic medical record.Research design and methodsThe OPTIMISTIC project was a Centers for Medicare and Medicaid Services demonstration to reduce avoidable hospital transfers of nursing home residents. The OPTIMISTIC team conducted a series of root cause analyses of transfer events, leading to development of a 27-item instrument to identify common characteristics of PAHs (Stage 1). To refine the instrument, project nurses used the electronic medical record (EMR) to score the avoidability of transfers to the hospital for 154 nursing home residents from 7 nursing homes from May 2019 through January 2020, including their overall impression of whether the transfer was avoidable (Stage 2). Each transfer was rated independently by 2 nurses and assessed for interrater reliability with a kappa statistic.ResultsKappa scores ranged from -0.045 to 0.556. After removing items based on our criteria, 12 final items constituted the Avoidable Transfer Scale. To assess validity, we compared the 12-item scale to nurses' overall judgment of avoidability of the transfer. The 12-item scale scores were significantly higher for submissions rated as avoidable than those rated unavoidable by the nurses (mean 5.3 vs 2.6, p < .001).Discussion and implicationsThe 12-item Avoidable Transfer Scale provides an efficient approach to identify and characterize PAHs using available data from the EMR. Increased ability to quantitatively assess the avoidability of resident transfers can aid nursing homes in quality improvement initiatives to treat more acute changes in a resident's condition in place.

Project description:ImportanceIdentification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit.ObjectiveTo externally validate the HOSPITAL score in an international multicenter study to assess its generalizability.Design, setting, and participantsInternational retrospective cohort study of 117 065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded.ExposuresThe HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay.Main outcomes and measures30-day potentially avoidable readmission to the index hospital using the SQLape algorithm.ResultsOverall, 117 065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16 992 of 117 065 (14.5%) were followed by a 30-day readmission, and 11 307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n = 73 031 [62.4%]), intermediate (n = 27 612 [23.6%]), and high risk (n = 16 422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson χ2 test P = .89).Conclusions and relevanceThe HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.

Project description:BACKGROUND:Hospitalizations for ambulatory care sensitive conditions are commonly used to evaluate primary health care performance, as the hospital admission could be avoided if care was timely and adequate. Previous evidence indicates that avoidable hospitalizations carry a substantial direct financial burden in some countries. However, no attention has been given to the economic burden on society they represent. The aim of this study is to estimate the direct and lost productivity costs of avoidable hospital admissions in Portugal. METHODS:Hospitalizations occurring in Portugal in 2015 were analyzed. Avoidable hospitalizations were defined and their associated costs and years of potential life lost were calculated. Direct costs were obtained using official hospitalization prices. For lost productivity, there were estimated costs for absenteeism and premature death. Costs were analyzed by components, by conditions and by variations on estimation parameters. RESULTS:The total estimated cost associated with avoidable hospital admissions was €250 million (€2515 per hospitalization), corresponding to 6% of the total budget of public hospitals in Portugal. These hospitalizations led to 109,641?years of potential life lost. Bacterial pneumonia, congestive heart failure and urinary tract infection accounted for 77% of the overall costs. Nearly 82% of avoidable hospitalizations were in patients aged 65?years or older, therefore did not account for the lost productivity costs. Nearly 84% of the total cost comes from the direct cost of the hospitalization. Lost productivity costs are estimated to be around €40 million. CONCLUSION:The age distribution of avoidable hospitalizations had a significant effect on costs components. Not only did hospital admissions have a substantial direct economic impact, they also imposed a considerable economic burden on society. Substantial financial resources could potentially be saved if the country reduced avoidable hospitalizations.

Project description:IntroductionInternationally there is pressure to contain costs due to rising numbers of hospital admissions. Alongside age, socioeconomic disadvantage is the strongest risk factor for avoidable hospital admission. This equity-focussed systematic review is required for policymakers to understand what has been shown to work to reduce inequalities in hospital admissions, what does not work and where the current gaps in the evidence-base are.Methods and analysisAn initial framework shows how interventions are hypothesised to reduce socioeconomic inequalities in avoidable hospital admissions. Studies will be included if the intervention focusses exclusively on socioeconomically disadvantaged populations or if the study reports differential effects by socioeconomic status (education, income, occupation, social class, deprivation, poverty or an area-based proxy for deprivation derived from place of residence) with respect to hospital admission or readmission (overall or condition-specific for those classified as ambulatory care sensitive). Studies involving individuals of any age, undertaken in OECD (Organisation for Economic Co-operation and Development) countries, published from 2000 to 29th February 2020 in any language will be included. Electronic searches will include MEDLINE, Embase, CINAHL, Cochrane CENTRAL and the Web of Knowledge platform. Electronic searches will be supplemented with full citation searches of included studies, website searches and retrieval of relevant unpublished information. Study inclusion, data extraction and quality appraisal will be conducted by two reviewers. Narrative synthesis will be conducted and also meta-analysis where possible. The main analysis will examine the effectiveness of interventions at reducing socioeconomic inequalities in hospital admissions. Interventions will be characterised by their domain of action and approach to addressing inequalities. For included studies, contextual information on where, for whom and how these interventions are organised, implemented and delivered will be examined where possible.Ethics and disseminationEthical approval was not required for this protocol. The research will be disseminated via peer-reviewed publication, conferences and an open-access policy-orientated paper.Prospero registration numberCRD42019153666.

Dataset Information

Predicting avoidable hospital events in Maryland.

Objective

Data sources

Study design

Data collection/extraction methods

Principal findings

Conclusions

Publications

Predicting avoidable hospital events in Maryland.

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