Framework for Smart Electronic Health Record- Linked Predictive Models to Optimize Care for Complex Digestive Diseases

2015 ◽  
Author(s):  
John D. Betteridge
Keyword(s):  
Electronic Health Record ◽  
Predictive Models ◽  
Health Record ◽  
Digestive Diseases ◽  
Electronic Health

scholarly journals Electronic health record-based predictive models for acute kidney injury screening in pediatric inpatients

2017 ◽  
Vol 82 (3) ◽  
pp. 465-473 ◽  
Author(s):  
Li Wang ◽  
Tracy L McGregor ◽  
Deborah P Jones ◽  
Brian C Bridges ◽  
Geoffrey M Fleming ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Electronic Health Record ◽  
Predictive Models ◽  
Kidney Injury ◽  
Health Record ◽  
Electronic Health ◽  
Injury Screening

scholarly journals Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review

Informatics ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 25
Author(s):  
Terrence C. Lee ◽  
Neil U. Shah ◽  
Alyssa Haack ◽  
Sally L. Baxter
Keyword(s):  
Systematic Review ◽  
Electronic Health Record ◽  
Clinical Outcomes ◽  
Predictive Models ◽  
Performance Metrics ◽  
Predictive Analytics ◽  
Health Record ◽  
Clinical Implementation ◽  
Level Of Evidence ◽  
Electronic Health

Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings.

scholarly journals Analyses of Original and Computationally-Derived Electronic Health Record Data: The National COVID Cohort Collaborative (Preprint)

2021 ◽  
Author(s):  
Randi Foraker ◽  
Aixia Guo ◽  
Jason Thomas ◽  
Noa Zamstein ◽  
Philip R.O. Payne ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Predictive Models ◽  
Clinical Laboratory ◽  
Synthetic Data ◽  
Original Data ◽  
Health Record ◽  
Use Case ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Synthetic Derivatives

BACKGROUND Background: Synthetic data can be used by collaborators to generate and share data in support of answering critical research questions to address the COVID-19 pandemic. Computationally-derived (“synthetic”) data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record (EHR) data. OBJECTIVE Objectives: To compare the results of analyses using synthetic derivatives to analyses using the original data downloaded from a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) to assess the strengths and limitations of leveraging computationally-derived data for research purposes. METHODS Methods: We used the National COVID Cohort Collaborative’s (N3C) instance of MDClone, comprising EHR data from 34 N3C institutional partners. We tested three use cases, including (1) exploring the distributions of key features of the COVID-positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-related measures and outcomes, and constructing their respective epidemic curves. We compared the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, temporal and spatial representations of the data. RESULTS Results: For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. While the synthetic and original data yielded overall nearly the same results, there were exceptions which included an odds ratio on either side of the null in multivariable analyses (0.97 versus 1.01) and epidemic curves constructed for zip codes with low population counts. CONCLUSIONS Discussion & Conclusion: This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights. CLINICALTRIAL N/A

scholarly journals Prediction of Antibiotic Susceptibility for Urinary Tract Infection in a Hospital Setting

2020 ◽  
Vol 64 (7) ◽  
Author(s):  
Courtney Hebert ◽  
Yuan Gao ◽  
Protiva Rahman ◽  
Courtney Dewart ◽  
Mark Lustberg ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Electronic Health Record ◽  
Statistical Learning ◽  
Predictive Models ◽  
Retrospective Cohort ◽  
Hospitalized Patients ◽  
Health Record ◽  
Data Set ◽  
Electronic Health ◽  
Tract Infections

ABSTRACT Empiric antibiotic prescribing can be supported by guidelines and/or local antibiograms, but these have limitations. We sought to use data from a comprehensive electronic health record to use statistical learning to develop predictive models for individual antibiotics that incorporate patient- and hospital-specific factors. This paper reports on the development and validation of these models with a large retrospective cohort. This was a retrospective cohort study including hospitalized patients with positive urine cultures in the first 48 h of hospitalization at a 1,500-bed tertiary-care hospital over a 4.5-year period. All first urine cultures with susceptibilities were included. Statistical learning techniques, including penalized logistic regression, were used to create predictive models for cefazolin, ceftriaxone, ciprofloxacin, cefepime, and piperacillin-tazobactam. These were validated on a held-out cohort. The final data set used for analysis included 6,366 patients. Final model covariates included demographics, comorbidity score, recent antibiotic use, recent antimicrobial resistance, and antibiotic allergies. Models had acceptable to good discrimination in the training data set and acceptable performance in the validation data set, with a point estimate for area under the receiver operating characteristic curve (AUC) that ranged from 0.65 for ceftriaxone to 0.69 for cefazolin. All models had excellent calibration. We used electronic health record data to create predictive models to estimate antibiotic susceptibilities for urinary tract infections in hospitalized patients. Our models had acceptable performance in a held-out validation cohort.

Business: The Electronic Health Record: Front Line Experience

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Electronic Health Record ◽  
Financial Incentives ◽  
Best Practice ◽  
Health Care Systems ◽  
The United States ◽  
Health Record ◽  
Health Trends ◽  
Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

Sign in / Sign up

Export Citation Format

Share Document