scholarly journals Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
William Galanter ◽  
Jorge Mario Rodríguez-Fernández ◽  
Kevin Chow ◽  
Samuel Harford ◽  
Karl M. Kochendorfer ◽  
...  
Keyword(s):  
Literature Review ◽  
Prediction Method ◽  
Clinical Decision ◽  
Chinese Patients ◽  
Sufficient Information ◽  
Receiver Operator Curve ◽  
Healthcare Organizations ◽  
Order Sets ◽  
External Model ◽  
Operator Curve

Abstract Background Many models are published which predict outcomes in hospitalized COVID-19 patients. The generalizability of many is unknown. We evaluated the performance of selected models from the literature and our own models to predict outcomes in patients at our institution. Methods We searched the literature for models predicting outcomes in inpatients with COVID-19. We produced models of mortality or criticality (mortality or ICU admission) in a development cohort. We tested external models which provided sufficient information and our models using a test cohort of our most recent patients. The performance of models was compared using the area under the receiver operator curve (AUC). Results Our literature review yielded 41 papers. Of those, 8 were found to have sufficient documentation and concordance with features available in our cohort to implement in our test cohort. All models were from Chinese patients. One model predicted criticality and seven mortality. Tested against the test cohort, internal models had an AUC of 0.84 (0.74–0.94) for mortality and 0.83 (0.76–0.90) for criticality. The best external model had an AUC of 0.89 (0.82–0.96) using three variables, another an AUC of 0.84 (0.78–0.91) using ten variables. AUC’s ranged from 0.68 to 0.89. On average, models tested were unable to produce predictions in 27% of patients due to missing lab data. Conclusion Despite differences in pandemic timeline, race, and socio-cultural healthcare context some models derived in China performed well. For healthcare organizations considering implementation of an external model, concordance between the features used in the model and features available in their own patients may be important. Analysis of both local and external models should be done to help decide on what prediction method is used to provide clinical decision support to clinicians treating COVID-19 patients as well as what lab tests should be included in order sets.

Riboflavin-responsive multiple Acyl-CoA dehydrogenation deficiency in 13 cases, and a literature review in mainland Chinese patients

2014 ◽  
Vol 59 (5) ◽  
pp. 256-261 ◽  
Author(s):  
Min Zhu ◽  
Xuan Zhu ◽  
Xueliang Qi ◽  
Ding Weijiang ◽  
Yajing Yu ◽  
...  
Keyword(s):  
Literature Review ◽  
Chinese Patients ◽  
Mainland Chinese

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 127-128
Keyword(s):  
Decision Support ◽  
Postoperative Delirium ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Free Text ◽  
Health Records ◽  
Support Tool ◽  
Team Selection ◽  
Order Sets

Chen JH, Alagappan M, Goldstein MK, Asch SM, Altman RB. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 2017 Jun;102:71-9 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28495350/ Ebadi A, Tighe PJ, Zhang L, Rashidi P. DisTeam: A decision support tool for surgical team selection. Artif Intell Med 2017 Feb;76:16-26 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28363285/ Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Inform Assoc 2017 Jul 1;24(4):806-12 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocx010 Mikalsen KØ, Soguero-Ruiz C, Jensen K, Hindberg K, Gran M, Revhaug A, Lindsetmo RO, Skrøvseth SO, Godtliebsen F, Jenssen R. Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput Methods Programs Biomed 2017 Dec;152:105-14 https://linkinghub.elsevier.com/retrieve/pii/S0169-2607(17)31154-9

Transitioning from one Electronic Health Record (EHR) to Another: A Narrative Literature Review

2018 ◽  
Vol 62 (1) ◽  
pp. 489-493 ◽  
Author(s):  
Jason J. Saleem ◽  
Jennifer Herout
Keyword(s):  
Health Care ◽  
Literature Review ◽  
Electronic Health Record ◽  
Health Care Systems ◽  
Health Care Organization ◽  
Data Migration ◽  
Health Record ◽  
Care Organization ◽  
Healthcare Organizations ◽  
Electronic Health

This paper reports the results of a literature review of health care organizations that have transitioned from one electronic health record (EHR) to another. Ten different EHR to EHR transitions are documented in the academic literature. In eight of the 10 transitions, the health care organization transitioned to Epic, a commercial EHR which is dominating the market for large and medium hospitals and health care systems. The focus of the articles reviewed falls into two main categories: (1) data migration from the old to new EHR and (2) implementation of the new EHR as it relates to patient safety, provider satisfaction, and other measures pre-and post-transition. Several conclusions and recommendations are derived from this review of the literature, which may be informative for healthcare organizations preparing to replace an existing EHR. These recommendations are likely broadly relevant to EHR to EHR transitions, regardless of the new EHR vendor.

scholarly journals Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial

2020 ◽  
Author(s):  
Nicolas Delvaux ◽  
Veerle Piessens ◽  
Tine De Burghgraeve ◽  
Pavlos Mamouris ◽  
Bert Vaes ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Laboratory Test ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Diagnostic Error ◽  
Order Sets ◽  
Cluster Randomized ◽  
Physician Order Entry ◽  
Test Ordering

Abstract Background Inappropriate laboratory test ordering poses an important burden for healthcare. Clinical decision support systems (CDSS) have been cited as promising tools to improve laboratory test ordering behavior. The objectives of this study were to evaluate the effects of an intervention that integrated a clinical decision support service into a computerized physician order entry (CPOE) on the appropriateness and volume of laboratory test ordering, and on diagnostic error in primary care.Methods This study was a pragmatic, cluster randomized, open label, controlled clinical trial. Setting 280 general practitioners (GPs) from 72 primary care practices in Belgium. Patients Patients aged ≥18 years with a laboratory test order for at least one of 17 indications; cardiovascular disease management, hypertension, check-up, chronic kidney disease (CKD), thyroid disease, type 2 diabetes mellitus, fatigue, anemia, liver disease, gout, suspicion of acute coronary syndrome (ACS), suspicion of lung embolism, rheumatoid arthritis, sexually transmitted infections (STI), acute diarrhea, chronic diarrhea, and follow-up of medication. Interventions The CDSS was integrated into a computerized physician order entry (CPOE) in the form of evidence-based order sets that suggested appropriate tests based on the indication provided by the general physician. Measurements The primary outcome of the ELMO study was the proportion of appropriate tests over the total number of ordered tests and inappropriately not-requested tests. Secondary outcomes of the ELMO study included diagnostic error, test volume and cascade activities.Results CDSS increased the proportion of appropriate tests by 0.21 (95% CI 0.16 - 0.26, p<.0001) for all tests included in the study. GPs in the CDSS arm ordered 7 (7.15 (95% CI 3.37 - 10.93, p=.0002)) tests fewer per panel. CDSS did not increase diagnostic error. The absolute difference in proportions was a decrease of 0.66% (95% CI 1.4% decrease - 0.05% increase) in possible diagnostic error.Conclusions A CDSS in the form of order sets, integrated within the CPOE improved appropriateness and decreased volume of laboratory test ordering without increasing diagnostic error. Trial Registration Clinicaltrials.gov Identifier: NCT02950142, registered on October 25, 2016Funding source This study was funded through the Belgian Health Care Knowledge Centre (KCE) Trials Programme agreement KCE16011.

scholarly journals Factors Associated With Intraoperative Conversion to Total Thyroidectomy in Benign Goiters

2019 ◽  
Vol 2 (1) ◽  
pp. 105-109
Author(s):  
Samuel Olatoke ◽  
Olayide Agodirin ◽  
Ganiyu Rahman ◽  
Benjamin Bolaji ◽  
Habeeb Olufemi
Keyword(s):  
Total Thyroidectomy ◽  
Nodular Goiter ◽  
Degenerative Changes ◽  
P Value ◽  
Receiver Operator Curve ◽  
Factors Associated ◽  
Patient Demographics ◽  
Partial Thyroidectomy ◽  
Intraoperative Conversion ◽  
Operator Curve

Background: Decision to undertake total thyroidectomy when gross inspection of the gland raises suspicion of widespread degenerative changes is often intraoperative. Knowing the factors associated with intraoperative conversion to total thyroidectomy may assist preoperative counselling. This study describes the probability of conversion to total thyroidectomy and factors associated with con-version among patients hitherto planned for partial thyroidectomy. Methods: We reviewed 191 records and extracted data on patient demographics, the pre-operative radiograph findings, the weight of excised gland and the operation performed. Descriptive and inferential statistics were performed. Receiver operator curve was used to assess for cut-off point. P-value was set at 0.05. Results: A total of 191 records was reviewed consisting of 181 females (94.8% 95% CI 90.6-97.5) and 10 males (5.2%, 95%CI 2.5-9.4). Only nodular goiters required conversion to total thyroidectomy. The over-all probability of total thyroidectomy was 11%(95% CI 7.0-16.3). The probability of total thyroidectomy in female was 10.5%(95% CI 6.4-16.9) while in male was 20%(95% CI2.5-55.6). The probability of total thyroidectomy in a female with nodular goiter was 8.1%(95% CI 4.8-13.5), compared to 28.6%(95% CI 3.7-71) in males. The risk of total thyroidectomy was associated with the weight of the excised gland. Conclusion: Only nodular goiters required intraoperative conversion to total thyroidecto-my and the probability of conversion was higher in males.

scholarly journals Instrument for Measuring Patient Safety Culture: Literature Review

2021 ◽  
Vol 10 (2) ◽  
pp. 158-176
Author(s):  
Yumna Nur Millati Hanifa ◽  
Inge Dhamanti
Keyword(s):  
Patient Safety ◽  
Literature Review ◽  
Safety Culture ◽  
Quality Care ◽  
Patient Safety Culture ◽  
Health Facilities ◽  
Healthcare Organizations ◽  
Just Culture ◽  
Safety Attitudes ◽  
Four Dimensions

The implementation of safe and quality care with attention to patient safety, requires organization’s effort to create and cultivating patient safety culture. The purpose of this article was to map the instruments used in measuring patient safety culture in healthcare organizations. The method used integrated literature review from various sources of research articles published from 2015 to 2020. The article included if it was available in full text and open access as well as articles described the instruments of patient safety culture or measurement of patient safety culture using one of the instruments of measurement of patient safety culture. The results of the literature review unravel the findings of three instruments such as HSOPSC (Hospital Survey on Patient Safety Culture), MaPSaF (Manchester Patient Safety Assessment Framework) and SAQ (Safety Attitudes Questionnaire). We concluded all three instruments contained four dimensions of patient safety culture, namely open culture, just culture, reporting culture and learning culture and were widely used to measure patient safety culture in hospitals, primary health facilities and other health facilities.

scholarly journals Validation of parsimonious prognostic models for patients infected with COVID-19

2021 ◽  
Vol 28 (1) ◽  
pp. e100267
Author(s):  
Keerthi Harish ◽  
Ben Zhang ◽  
Peter Stella ◽  
Kevin Hauck ◽  
Marwa M Moussa ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Clinical Decision ◽  
Prognostic Models ◽  
Clinical Prediction ◽  
Inclusion Criteria ◽  
Local Data ◽  
External Data ◽  
Operator Curve

ObjectivesPredictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data.MethodsWe performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020.ResultsMost models failed validation when applied to our institution’s data. Included studies reported an average validation area under the receiver–operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies’ reported AUROC values.DiscussionPublished and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations.ConclusionsClinicians should employ caution when applying models for clinical prediction without careful validation on local data.

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