Predicting need for advanced illness or palliative care in a primary care population using electronic health record data

Jung, Kenneth; Sudat, Sylvia; Kwon, Nicole; Stewart, Walter F.; Shah, Nigam H.

doi:10.1016/j.jbi.2019.103115

Cited by 18 publications

(19 citation statements)

References 58 publications

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“…This is a popular statistical learning algorithm that has outperformed other methods in large routine healthcare datasets with many predictors. 24,26,28 The tuning parameters of the algorithm are given in Appendix 1.…”

Section: Use Of Boosted Trees To Account For Interactionsmentioning

confidence: 99%

“…These models have accounted for the timing of diagnosis records [24][25][26] and were estimated using machine learning methods that do not assume a simple functional form for associations between outcomes and predictors. [24][25][26][27][28] This could help these approaches to better predict outcomes than more conventional methods, but this has not been tested or quantified empirically in the existing literature. Moreover, more complex models may be less interpretable and reproducible than simpler ones, which may negate any benefits in terms of prediction performance.…”

Section: Introductionmentioning

confidence: 99%

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A novel approach selected small sets of diagnosis codes with high prediction performance in large healthcare datasets

Cowling

Cromwell

Sharples

et al. 2020

Journal of Clinical Epidemiology

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Section: Use Of Boosted Trees To Account For Interactionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel approach selected small sets of diagnosis codes with high prediction performance in large healthcare datasets

Cowling

Cromwell

Sharples

et al. 2020

Journal of Clinical Epidemiology

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“…These limitations may have significantly reduced the sensitivity of this method for ascertainment of those in end-of-life care. The current study complements the works of Avati and colleagues and Jung and colleagues, who developed prognostic predictive models for proactively determining patients who should be referred for palliative care [29,30]. Our use of a predictive model to improve the performance of a heuristic model increases our capability to determine those in end-of-life care.…”

Section: Discussionmentioning

confidence: 65%

Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets

et al. 2020

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Introduction In observational studies with mortality endpoints, one needs to consider how to account for subjects whose interventions appear to be part of 'end-of-life' care. Objective The objective of this study was to develop a diagnostic predictive model to identify those in end-of-life care at the time of a drug exposure. Methods We used data from four administrative claims datasets from 2000 to 2017. The index date was the date of the first prescription for the last new drug subjects received during their observation period. The outcome of end-of-life care was determined by the presence of one or more codes indicating terminal or hospice care. Models were developed using regularized logistic regression. Internal validation was through examination of the area under the receiver operating characteristic curve (AUC) and through model calibration in a 25% subset of the data held back from model training. External validation was through examination of the AUC after applying the model learned on one dataset to the three other datasets. Results The models showed excellent performance characteristics. Internal validation resulted in AUCs ranging from 0.918 (95% confidence interval [CI] 0.905-0.930) to 0.983 (95% CI 0.978-0.987) for the four different datasets. Calibration results were also very good, with slopes near unity. External validation also produced very good to excellent performance metrics, with AUCs ranging from 0.840 (95% CI 0.834-0.846) to 0.956 (95% CI 0.952-0.960). Conclusion These results show that developing diagnostic predictive models for determining subjects in end-of-life care at the time of a drug treatment is possible and may improve the validity of the risk profile for those treatments.

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“…For instance, early palliative care scores were focused on patients with cancer, whether late stage or early stage [17]. Subsequently, scores have been developed speci cally for inpatient, either general oor or ICU units or community based [18][19][20]. Although these scores have greater utility, they limit the ability to transfer to other populations which may limit legitimate palliative care need.…”

Section: Risk Scoresmentioning

confidence: 99%

“…Recently with the advent of arti cial intelligence (AI) and near universal adoption of electronic medical records (EMR), scores are now being developed that utilizes large and complex types of routinely collected data for predictions. Avati et al developed a deep learning algorithm on EMR data utilizing a model with 13,654 features predicting mortality, and Jung et al developed an outpatient mortality prediction algorithm utilizing 1,880 features and using gradient boosting machines (GBMs) [20,21].…”

Section: Risk Scoresmentioning

confidence: 99%

Improving Time to Palliative Care Review With Predictive Modeling: Study Protocol for a Stepped-wedge, Pragmatic Randomized Controlled Trial

Wilson¹,

Philpot²,

Ramar³

et al. 2021

Preprint

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Background: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for Palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care. Methods: To that Aim we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist, and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary’s Hospital & Methodist Hospital, Mayo Clinic, Rochester, Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance.Discussion: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; Clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice whilst retaining scientific rigor.Trial registration NCT03976297 (ClinicalTrials.gov), registered on June 6, 2019 prior to trial start.

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Predicting need for advanced illness or palliative care in a primary care population using electronic health record data

Cited by 18 publications

References 58 publications

A novel approach selected small sets of diagnosis codes with high prediction performance in large healthcare datasets

A novel approach selected small sets of diagnosis codes with high prediction performance in large healthcare datasets

Developing Predictive Models to Determine Patients in End-of-Life Care in Administrative Datasets

Improving Time to Palliative Care Review With Predictive Modeling: Study Protocol for a Stepped-wedge, Pragmatic Randomized Controlled Trial

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