Postdischarge Communication Between Home Health Nurses and Physicians: Measurement, Quality, and Outcomes

Press, Matthew J.; Gerber, Linda M.; Peng, Timothy R.; Feldman, Penny Hollander; Ouchida, Karin; Sridharan, Sridevi; Bao, Yuhua; Barrón, Yolanda; Casalino, Lawrence P.

doi:10.1111/jgs.13491

Cited by 27 publications

(39 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The assigned categories of communication were validated by manual review of select records (Press et al. ). EMRs provide big data that can be rapidly analyzed using NLP.…”

Section: Datamentioning

confidence: 99%

“…Then, the free-text comments were processed to identify matches between phrase components and the master concepts. The assigned categories of communication were validated by manual review of select records (Press et al 2015). EMRs provide big data that can be rapidly analyzed using NLP.…”

Section: Datamentioning

confidence: 99%

“…We previously identified a small, statistically insignificant positive association between communication failure and readmission (Press et al. ). In this study, we expand Press et al.…”

mentioning

confidence: 93%

“…In this study, we investigate one aspect of quality in this program: communication between HHC nurses and physicians. We previously identified a small, statistically insignificant positive association between communication failure and readmission (Press et al 2015). In this study, we expand Press et al (2015) by separately analyzing the probability of a readmission for high-risk and low-risk patients using stratified and interaction models.…”

mentioning

confidence: 94%

“…In this study, we expand Press et al. () by separately analyzing the probability of a readmission for high‐risk and low‐risk patients using stratified and interaction models. We also explore how results are influenced by propensity score matching.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Home Health Care: Nurse–Physician Communication, Patient Severity, and Hospital Readmission

Pesko

Gerber

Peng

et al. 2017

Health Services Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…The assigned categories of communication were validated by manual review of select records (Press et al. ). EMRs provide big data that can be rapidly analyzed using NLP.…”

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

“…We previously identified a small, statistically insignificant positive association between communication failure and readmission (Press et al. ). In this study, we expand Press et al.…”

mentioning

confidence: 93%

mentioning

confidence: 94%

mentioning

confidence: 99%

See 3 more Smart Citations

Home Health Care: Nurse–Physician Communication, Patient Severity, and Hospital Readmission

Pesko

Gerber

Peng

et al. 2017

Health Services Research

Self Cite

View full text Add to dashboard Cite

show abstract

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

et al. 2020

View full text Add to dashboard Cite

Aims This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling. Methods and results Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full‐text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time‐point comparisons, ML‐derived c‐indices were higher than CSM‐derived c‐indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in‐hospital mortality to 1 year survival; of these nine, seven reported higher c‐indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c‐indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c‐indices 0.913 vs. 0.835). Conclusions ML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML‐based studies of prediction modelling. We suggest that ML‐based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867

show abstract

A Geriatrician’s Guide to Accountable Care Implementation: Thickets and Pathways

Hill

2017

Primary Care for Older Adults

View full text Add to dashboard Cite

Postdischarge Communication Between Home Health Nurses and Physicians: Measurement, Quality, and Outcomes

Cited by 27 publications

References 17 publications

Home Health Care: Nurse–Physician Communication, Patient Severity, and Hospital Readmission

Home Health Care: Nurse–Physician Communication, Patient Severity, and Hospital Readmission

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

A Geriatrician’s Guide to Accountable Care Implementation: Thickets and Pathways

Contact Info

Product

Resources

About