A Comparison of Models Predicting One-Year Mortality at Time of Admission

Pierce, Robert P.; Raithel, Seth; Brandt, Lea; Clary, Kevin; Craig, Kevin

doi:10.1016/j.jpainsymman.2021.11.006

Cited by 3 publications

(2 citation statements)

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“…The potential of machine learning predictions to expand SIC has been trialed at a handful of academic hospitals in recent years, with studies showing challenging feasibility but likely improved clinical outcomes. However, firm conclusions have been limited by the absence of controls and the recent variability in health care and patient mix caused by the COVID-19 pandemic . We have recently published the structure of an accurate machine learning algorithm for predicting short-term mortality in a modern inpatient population .…”

Section: Introductionmentioning

confidence: 99%

“…However, firm conclusions have been limited by the absence of controls and the recent variability in health care and patient mix caused by the COVID-19 pandemic. 25 , 26 , 27 , 28 , 29 , 30 We have recently published the structure of an accurate machine learning algorithm for predicting short-term mortality in a modern inpatient population. 31 By implementing this algorithm in a community hospital with propensity-matched controls, we sought to investigate whether a mortality risk-targeted EHR prompt was associated with increased inpatient goals of care documentation.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm

et al. 2023

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ImportanceGoal-concordant care is an ongoing challenge in hospital settings. Identification of high mortality risk within 30 days may call attention to the need to have serious illness conversations, including the documentation of patient goals of care.ObjectiveTo examine goals of care discussions (GOCDs) in a community hospital setting with patients identified as having a high risk of mortality by a machine learning mortality prediction algorithm.Design, Setting, and ParticipantsThis cohort study took place at community hospitals within 1 health care system. Participants included adult patients with a high risk of 30-day mortality who were admitted to 1 of 4 hospitals between January 2 and July 15, 2021. Patient encounters of inpatients in the intervention hospital where physicians were notified of the computed high risk mortality score were compared with patient encounters of inpatients in 3 community hospitals without the intervention (ie, matched control).InterventionPhysicians of patients with a high risk of mortality within 30 days received notification and were encouraged to arrange for GOCDs.Main Outcomes and MeasuresThe primary outcome was the percentage change of documented GOCDs prior to discharge. Propensity-score matching was completed on a preintervention and postintervention period using age, sex, race, COVID-19 status, and machine learning-predicted mortality risk scores. A difference-in-difference analysis validated the results.ResultsOverall, 537 patients were included in this study with 201 in the preintervention period (94 in the intervention group; 104 in the control group) and 336 patients in the postintervention period. The intervention and control groups included 168 patients per group and were well-balanced in age (mean [SD], 79.3 [9.60] vs 79.6 [9.21] years; standardized mean difference [SMD], 0.03), sex (female, 85 [51%] vs 85 [51%]; SMD, 0), race (White patients, 145 [86%] vs 144 [86%]; SMD 0.006), and Charlson comorbidities (median [range], 8.00 [2.00-15.0] vs 9.00 [2.00 to 19.0]; SMD, 0.34). Patients in the intervention group from preintervention to postintervention period were associated with being 5 times more likely to have documented GOCDs (OR, 5.11 [95% CI, 1.93 to 13.42]; P = .001) by discharge compared with matched controls, and GOCD occurred significantly earlier in the hospitalization in the intervention patients as compared with matched controls (median, 4 [95% CI, 3 to 6] days vs 16 [95% CI, 15 to not applicable] days; P &lt; .001). Similar findings were observed for Black patient and White patient subgroups.Conclusions and RelevanceIn this cohort study, patients whose physicians had knowledge of high-risk predictions from machine learning mortality algorithms were associated with being 5 times more likely to have documented GOCDs than matched controls. Additional external validation is needed to determine if similar interventions would be helpful at other institutions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm

et al. 2023

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Predictive Models for Palliative Care Needs of Advanced Cancer Patients Receiving Chemotherapy

Kawashima,

Furukawa,

Imaizumi

et al. 2024

Journal of Pain and Symptom Management

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Web of Science-based Information Technology in Hospice Visual Analytics (Preprint)

cao,

huang,

wang

et al. 2023

Preprint

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BACKGROUND At present, China has stepped into the development stage of deep aging, and the surge in the number of elderly population with chronic diseases, malignant tumors and other incurable diseases will stimulate the social demand for hospice services[1] . In recent years, with the rapid development of information technology, information technology has been applied to the care of all kinds of diseases, and it also plays a great role in the field of hospice care. Information technology refers to all possible technologies used to process and communicate information, such as computer technology, smart phones, multimedia, network hardware, software, internet, etc[2] . The World Health Organization proposes to help hospice patients live actively until death by implementing information technology and infrastructure[3,4] . Information technology is the key to the development of hospice care and can solve the major problems that hinder the development of hospice care, such as the difficulty of information sharing, the shortage of medical resources, the difficulty of communication and so on[5] . Currently, studies[6-8] have explored the results of the application of information technology in hospice care, but the visual analysis of the current status and hotspots of research in this field is still lacking. Therefore, this study applies bibliometrics to analyze the application of information technology in hospice care globally to provide a reference for promoting the development of information technology in hospice care in China. OBJECTIVE Objective: to analyze the research hotspots of the application of information technology in the field of hospice care, and to provide reference for the research and practice of the application of information technology in the field of hospice care in China. METHODS METHODS: We systematically searched the Web of Science core database for literature on the application of information technology in the field of hospice care, and used Hiscite Pro2.1, Citespace 6.2.R2, VOS viewer 1.6.18, and the bibliometric online analysis platform software to conduct bibliometric analysis. RESULTS RESULTS: A total of 395 documents were included, and the number of publications showed an increasing trend year by year, in which the country with the most publications was the United States; a total of 2343 authors participated in the research in this field, and a clear core team has been formed; the institutions with a high number of publications were dominated by American colleges, and there was a close cooperation and exchange between institutions; the author with the most publications was Prof. David Casaret from the United States; the Journal of Palliative Medicine was the journal with the most publications; keyword clustering was the most important factor in the analysis of the bibliometrics. The Journal of Palliative Medicine is the journal with the most publications; the top 5 keywords in the cluster analysis are "quality of life", "end stage", "home hospice", "death", "living" and "care". The top 5 keywords analyzed were "quality of life", "end stage", "home hospice", "death", "life care", etc. Keywords that have emerged in the last 5 years include "machine learning", "advance health care planning", "communication", "artificial intelligence", "home care", and "quality of life". CONCLUSIONS CONCLUSION: Research on the use of information technology in hospice care is in a rapidly developing phase. At present, it is mainly applied to home hospice care , improving patients' quality of life, predicting survival , nurse-patient communication, and advance care planning (ACP).

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A Comparison of Models Predicting One-Year Mortality at Time of Admission

Cited by 3 publications

References 32 publications

Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm

Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm

Predictive Models for Palliative Care Needs of Advanced Cancer Patients Receiving Chemotherapy

Web of Science-based Information Technology in Hospice Visual Analytics (Preprint)

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