Processing electronic medical records to improve predictive analytics outcomes for hospital readmissions

Zolbanin, Hamed Majidi; Delen, Dursun

doi:10.1016/j.dss.2018.06.010

Cited by 27 publications

(22 citation statements)

References 38 publications

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“…We divided these studies on the basis of their population cohort into all patient populations (n=17, including one intensive care unit and one emergency department readmission) and patient specific populations (n=24). Most patient specific models were for heart conditions (n=13) 24252627303234373843454647. The remainder were based on readmission among patients with diabetes (n=4),28334041 kidney transplantation (1),44 hemodialysis (1),29 low back surgery (1),36 pneumonia (2),3135 lupus (1),39 and psychiatric conditions (1) 42.…”

Section: Resultsmentioning

confidence: 99%

“…All validations were done internally; most were conducted through retrospective validation (n=37) and used split sample (n=24) or cross validation (n=11) methods. The C statistics ranged between 0.52 and 0.90,2324 with 17 studies reporting a C statistic of 0.75 or greater 1112141516171923293334363742434647…”

Section: Resultsmentioning

confidence: 99%

“…Twenty four studies developed predictive models of readmission among specific patient populations (table 2). Most (13/24 studies) of these models were developed for patients admitted with heart conditions 24252627303234373843454647. All patient specific studies included adults 18 years or older.…”

Section: Resultsmentioning

confidence: 99%

“…Ten studies used data from multiple hospitals 25262830323435373947. Data came from academic centers (n=9),242931333640414244 non-academic centers (n=3),273843 or a combination of both (n=10) 252628303235373947. Observed readmission rates for patient specific readmissions ranged between 5.9% and 54% 3638…”

Section: Resultsmentioning

confidence: 99%

“…Fifteen studies used various machine learning methods, including Bayesian conditional probability,1043 random forest,1415384647 neural network,15192739 deep learning, 41 AdoBoost,22 gradient boosting,3047 natural language processing,303642 and others 244546. The most popular machine learning methods used were random forest and neural networks.…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Mahmoudi

Kamdar

Kim

et al. 2020

BMJ

122

128

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Objective To provide focused evaluation of predictive modeling of electronic medical record (EMR) data to predict 30 day hospital readmission. Design Systematic review. Data source Ovid Medline, Ovid Embase, CINAHL, Web of Science, and Scopus from January 2015 to January 2019. Eligibility criteria for selecting studies All studies of predictive models for 28 day or 30 day hospital readmission that used EMR data. Outcome measures Characteristics of included studies, methods of prediction, predictive features, and performance of predictive models. Results Of 4442 citations reviewed, 41 studies met the inclusion criteria. Seventeen models predicted risk of readmission for all patients and 24 developed predictions for patient specific populations, with 13 of those being developed for patients with heart conditions. Except for two studies from the UK and Israel, all were from the US. The total sample size for each model ranged between 349 and 1 195 640. Twenty five models used a split sample validation technique. Seventeen of 41 studies reported C statistics of 0.75 or greater. Fifteen models used calibration techniques to further refine the model. Using EMR data enabled final predictive models to use a wide variety of clinical measures such as laboratory results and vital signs; however, use of socioeconomic features or functional status was rare. Using natural language processing, three models were able to extract relevant psychosocial features, which substantially improved their predictions. Twenty six studies used logistic or Cox regression models, and the rest used machine learning methods. No statistically significant difference (difference 0.03, 95% confidence interval −0.0 to 0.07) was found between average C statistics of models developed using regression methods (0.71, 0.68 to 0.73) and machine learning (0.74, 0.71 to 0.77). Conclusions On average, prediction models using EMR data have better predictive performance than those using administrative data. However, this improvement remains modest. Most of the studies examined lacked inclusion of socioeconomic features, failed to calibrate the models, neglected to conduct rigorous diagnostic testing, and did not discuss clinical impact.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Mahmoudi

Kamdar

Kim

et al. 2020

BMJ

122

128

View full text Add to dashboard Cite

show abstract

Toward an extended framework of exhaust data for predictive analytics: An empirical approach

O'Leary

2024

Intell Sys Acc Fin Mgmt

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SummaryWe investigate applying and extending an exhaust data framework, using an empirical analysis to explore and compare different predictive analytic capabilities of both internal and external exhaust data for estimating sales. We use internal exhaust data that explores the relationship between app usage and web traffic data and estimation of sales and find the ability to predict sales at least 4 days ahead. We also develop predictive models of sales, using external data of Google searches, extending the previous research to include additional macroeconomic Google variables and Wikipedia pageviews, finding that we can predict at least 4 months ahead, suggesting a portfolio of exhaust data be used. We introduce the roles of internal and external exhaust data, direct and indirect exhaust data and transformed exhaust data, into an exhaust data framework. We examine what appear to be different levels of information fineness and predictability from those exhaust data sources. We also note the importance of the types of devices (e.g., mobile) and the types of commerce (e.g., mobile commerce) in creating and finding different types of exhaust. Finally, we apply an existing exhaust data framework to develop macroeconomic data exhaust variables, as the means of capturing inflation and unemployment information, using Google searches.

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Identifying Essential Factors for Deriving Value from Big Data Analytics in Healthcare

Eschenbrenner

2019

HCI in Business, Government and Organizations. Information Systems and Analytics

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Processing electronic medical records to improve predictive analytics outcomes for hospital readmissions

Cited by 27 publications

References 38 publications

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review

Toward an extended framework of exhaust data for predictive analytics: An empirical approach

Identifying Essential Factors for Deriving Value from Big Data Analytics in Healthcare

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