scholarly journals Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study

10.2196/19516 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e19516
Author(s):  
Elisa Dolci ◽  
Barbara Schärer ◽  
Nicole Grossmann ◽  
Sarah Naima Musy ◽  
Franziska Zúñiga ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Chart Review ◽  
Fall Detection ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Trigger Tool ◽  
Patient Reported ◽  
Global Trigger Tool ◽  
Incident Reports ◽  
Electronic Health

Background Falls are common adverse events in hospitals, frequently leading to additional health costs due to prolonged stays and extra care. Therefore, reliable fall detection is vital to develop and test fall prevention strategies. However, conventional methods—voluntary incident reports and manual chart reviews—are error-prone and time consuming, respectively. Using a search algorithm to examine patients’ electronic health record data and flag fall indicators offers an inexpensive, sensitive, cost-effective alternative. Objective This study’s purpose was to develop a fall detection algorithm for use with electronic health record data, then to evaluate it alongside the Global Trigger Tool, incident reports, a manual chart review, and patient-reported falls. Methods Conducted on 2 campuses of a large hospital system in Switzerland, this retrospective diagnostic accuracy study consisted of 2 substudies: the first, targeting 240 patients, for algorithm development and the second, targeting 298 patients, for validation. In the development study, we compared the new algorithm’s in-hospital fall rates with those indicated by the Global Trigger Tool and incident reports; in the validation study, we compared the algorithm’s in-hospital fall rates with those from patient-reported falls and manual chart review. We compared the various methods by calculating sensitivity, specificity, and predictive values. Results Twenty in-hospital falls were discovered in the development study sample. Of these, the algorithm detected 19 (sensitivity 95%), the Global Trigger Tool detected 18 (90%), and incident reports detected 14 (67%). Of the 15 falls found in the validation sample, the algorithm identified all 15 (100%), the manual chart review identified 14 (93%), and the patient-reported fall measure identified 5 (33%). Owing to relatively high numbers of false positives based on falls present on admission, the algorithm’s positive predictive values were 50% (development sample) and 47% (validation sample). Instead of requiring 10 minutes per case for a full manual review or 20 minutes to apply the Global Trigger Tool, the algorithm requires only a few seconds, after which only the positive results (roughly 11% of the full case number) require review. Conclusions The newly developed electronic health record algorithm demonstrated very high sensitivity for fall detection. Applied in near real time, the algorithm can record in-hospital falls events effectively and help to develop and test fall prevention measures.

scholarly journals Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study (Preprint)

2020 ◽  
Author(s):  
Elisa Dolci ◽  
Barbara Schärer ◽  
Nicole Grossmann ◽  
Sarah Naima Musy ◽  
Franziska Zúñiga ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Chart Review ◽  
Fall Detection ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Trigger Tool ◽  
Patient Reported ◽  
Global Trigger Tool ◽  
Incident Reports ◽  
Electronic Health

BACKGROUND Falls are common adverse events in hospitals, frequently leading to additional health costs due to prolonged stays and extra care. Therefore, reliable fall detection is vital to develop and test fall prevention strategies. However, conventional methods—voluntary incident reports and manual chart reviews—are error-prone and time consuming, respectively. Using a search algorithm to examine patients’ electronic health record data and flag fall indicators offers an inexpensive, sensitive, cost-effective alternative. OBJECTIVE This study’s purpose was to develop a fall detection algorithm for use with electronic health record data, then to evaluate it alongside the Global Trigger Tool, incident reports, a manual chart review, and patient-reported falls. METHODS Conducted on 2 campuses of a large hospital system in Switzerland, this retrospective diagnostic accuracy study consisted of 2 substudies: the first, targeting 240 patients, for algorithm development and the second, targeting 298 patients, for validation. In the development study, we compared the new algorithm’s in-hospital fall rates with those indicated by the Global Trigger Tool and incident reports; in the validation study, we compared the algorithm’s in-hospital fall rates with those from patient-reported falls and manual chart review. We compared the various methods by calculating sensitivity, specificity, and predictive values. RESULTS Twenty in-hospital falls were discovered in the development study sample. Of these, the algorithm detected 19 (sensitivity 95%), the Global Trigger Tool detected 18 (90%), and incident reports detected 14 (67%). Of the 15 falls found in the validation sample, the algorithm identified all 15 (100%), the manual chart review identified 14 (93%), and the patient-reported fall measure identified 5 (33%). Owing to relatively high numbers of false positives based on falls present on admission, the algorithm’s positive predictive values were 50% (development sample) and 47% (validation sample). Instead of requiring 10 minutes per case for a full manual review or 20 minutes to apply the Global Trigger Tool, the algorithm requires only a few seconds, after which only the positive results (roughly 11% of the full case number) require review. CONCLUSIONS The newly developed electronic health record algorithm demonstrated very high sensitivity for fall detection. Applied in near real time, the algorithm can record in-hospital falls events effectively and help to develop and test fall prevention measures.

scholarly journals Validation of an Internationally Derived Patient Severity Phenotype to Support COVID-19 Analytics from Electronic Health Record Data

2021 ◽  
Author(s):  
Jeffrey G Klann ◽  
Griffin M Weber ◽  
Hossein Estiri ◽  
Bertrand Moal ◽  
Paul Avillach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Health Record ◽  
Chart Review ◽  
Learning Approach ◽  
Health Record ◽  
Learning Approaches ◽  
Electronic Health Record Data ◽  
Icu Admission ◽  
Machine Learning Approach ◽  
Electronic Health

Abstract Introduction The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. Objective We sought to develop and validate a computable phenotype for COVID-19 severity. Methods Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. Conclusion We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

scholarly journals New AI Technologies to Enrich Electronic Health Record Data Sets With Self-Report Scores in Geriatrics

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 275-275
Author(s):  
Ricardo Pietrobon
Keyword(s):  
Electronic Health Record ◽  
Self Report ◽  
Health Record ◽  
Data Sets ◽  
Electronic Health Record Data ◽  
Patient Reported ◽  
Record Data ◽  
Data Source ◽  
Electronic Health ◽  
Rich Data

Abstract Although electronic health record data present a rich data source for health service researchers, for the most part, they lack self-report information. Although recent CMS projects have provided hospitals with incentives to collect patient-reported outcomes for select procedures, the process often leads to a substantial percentage of missing data, also being expensive as it requires the assistance of research coordinators. In this presentation, we will cover Artificial Intelligence-based based technologies to reduce the burden of data collection, allowing for its expansion across clinics and conditions. The technology involves the use of algorithms to predict self-report scores based on widely available claims data. Following previous work predicting frailty scores from existing variables, we expand its use with scores related to quality of life, i.e. mental health and physical function, and cognition. Accuracy metrics are presented both in cross-validation as well as external samples.

scholarly journals A methodology to generate longitudinally updated ACLF prognostication scores from electronic health record data

2020 ◽  
Author(s):  
Jin Ge ◽  
Nader Najafi ◽  
Wendi Zhao ◽  
Ma Somsouk ◽  
Margaret Fang ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Chart Review ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Cohen’S Kappa ◽  
Data Repositories ◽  
Patient Admissions ◽  
Cohen's Kappa ◽  
Electronic Health ◽  
End Stage

AbstractBackground and AimsQueries of electronic health record (EHR) data repositories allow for automated data collection. These techniques have not been utilized in hepatology due to previous inability to capture hepatic encephalopathy (HE) grades, which are inputs for acute-on-chronic liver failure (ACLF) models. Here, we describe a methodology to utilizing EHR data to calculate rolling ACLF scores.MethodsWe examined 239 patient-admissions with end-stage liver disease 7/2014-6/2019. We mapped EHR flowsheet data to determine HE grades and calculated two longitudinally updated ACLF scores. We validated HE grades and ACLF diagnoses via chart review; and calculated sensitivity, specificity, and Cohen’s kappa.ResultsOf 239 patient-admissions analyzed, 37% women, 46% non-Hispanic White, median age 60 years, median MELD-Na at admission. Of the 239, 7% were diagnosed with NACSELD-ACLF at admission, 27% during the hospitalization, and 9% at discharge. Forty percent diagnosed with CLIF-C-ACLF at admission, 51% during the hospitalization, and 34% at discharge.From chart review of 51 admissions, we found sensitivities and specificities for any HE (grades 1-4) were 92-97% and 76-95%, respectively; for severe HE (grades 3-4) were 100% and 78-98%, respectively. Cohen’s kappa between flowsheet and chart review HE grades ranged 0.55-0.72. Sensitivities and specificities for NACSELD-ACLF diagnoses were 75-100% and 96-100%, respectively; for CLIF-C-ACLF diagnoses were 91-100% and 96-100%, respectively. We generated approximately 28 unique ACLF scores per patient per admission-day.ConclusionIn this study, we developed an informatics-based methodology for to calculate longitudinally updated ACLF scores. This opens new analytic potentials, such big data methods to develop electronic phenotypes for ACLF patients.

The APPROACH trial: Assessing pain, patient-reported outcomes, and complementary and integrative health

2020 ◽  
Vol 17 (4) ◽  
pp. 351-359
Author(s):  
Steven B Zeliadt ◽  
Scott Coggeshall ◽  
Eva Thomas ◽  
Hannah Gelman ◽  
Stephanie L Taylor
Keyword(s):  
Electronic Health Record ◽  
Real Time ◽  
Patient Reported Outcomes ◽  
Health Record ◽  
Business Practices ◽  
Electronic Health Record Data ◽  
Integrative Health ◽  
Patient Reported ◽  
Record Data ◽  
Electronic Health

Electronic health record data can be used in multiple ways to facilitate real-world pragmatic studies. Electronic health record data can provide detailed information about utilization of treatment options to help identify appropriate comparison groups, access historical clinical characteristics of participants, and facilitate measuring longitudinal outcomes for the treatments being studied. An additional novel use of electronic health record data is to assess and understand referral pathways and other business practices that encourage or discourage patients from using different types of care. We describe an ongoing study utilizing access to real-time electronic health record data about changing patterns of complementary and integrative health services to demonstrate how electronic health record data can provide the foundation for a pragmatic study when randomization is not feasible. Conducting explanatory trials of the value of emerging therapies within a healthcare system poses ethical and pragmatic challenges, such as withholding access to specific services that are becoming widely available to patients. We describe how prospective examination of real-time electronic health record data can be used to construct and understand business practices as potential surrogates for direct randomization through an instrumental variables analytic approach. In this context, an example of a business practice is the internal hiring of acupuncturists who also provide yoga or Tai Chi classes and can offer these classes without additional cost compared to community acupuncturists. Here, the business practice of hiring internal acupuncturists is likely to encourage much higher rates of combined complementary and integrative health use compared to community referrals. We highlight the tradeoff in efficiency of this pragmatic approach and describe use of simulations to estimate the potential sample sizes needed for a variety of instrument strengths. While real-time monitoring of business practices from electronic health records provides insights into the validity of key independence assumptions associated with the instrumental variable approaches, we note that there may be some residual confounding by indication or selection bias and describe how alternative sources of electronic health record data can be used to assess the robustness of instrumental variable assumptions to address these challenges. Finally, we also highlight that while some clinical outcomes can be obtained directly from the electronic health record, such as longitudinal opioid utilization and pain intensity levels for the study of the value of complementary and integrative health, it is often critical to supplement clinical electronic health record–based measures with patient-reported outcomes. The experience of this example in evaluating complementary and integrative health demonstrates the use of electronic health record data in several novel ways that may be of use for designing future pragmatic trials.

Automated chronic disease surveillance and visualization using electronic health record data

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Michael Klompas ◽  
Chaim Kirby ◽  
Jason McVetta ◽  
Paul Oppedisano ◽  
John Brownstein ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Electronic Health Record ◽  
Disease Surveillance ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Leveraging electronic health record data to inform hospital resource management

2021 ◽  
Author(s):  
José Carlos Ferrão ◽  
Mónica Duarte Oliveira ◽  
Daniel Gartner ◽  
Filipe Janela ◽  
Henrique M. G. Martins
Keyword(s):  
Resource Management ◽  
Electronic Health Record ◽  
Health Record ◽  
Hospital Resource ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health
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