Impact of family-centred postnatal training on maternal and neonatal health and care practices in district hospitals in two states in India: a pre-post study.

Background And Objectives: The Care Companion Program (CCP) is an in-hospital multitopic skill-based training programme provided to families to improve postdischarge maternal and neonatal health. The states of Punjab and Karnataka in India piloted the programme in 12 district hospitals in July 2017, and no study to date has evaluated its impact.

Methods: We compared telephonically self-reported maternal and neonatal care practices and health outcomes before and after the launch of the CCP programme in 11 facilities.

A survey of extant organizational and computational setups for deploying predictive models in health systems.

Objective: Artificial intelligence (AI) and machine learning (ML) enabled healthcare is now feasible for many health systems, yet little is known about effective strategies of system architecture and governance mechanisms for implementation. Our objective was to identify the different computational and organizational setups that early-adopter health systems have utilized to integrate AI/ML clinical decision support (AI-CDS) and scrutinize their trade-offs.

Materials And Methods: We conducted structured interviews with health systems with AI deployment experience about their organizational and computational setups for deploying AI-CDS at point of care.

Impact of diagnosis code grouping method on clinical prediction model performance: A multi-site retrospective observational study.

Objective: The primary purpose of this work is to systematically assess the performance trade-offs on clinical prediction tasks of four diagnosis code groupings: AHRQ-Elixhauser, Single-level CCS, truncated ICD-9-CM codes, and raw ICD-9-CM codes.

Materials And Methods: We used two distinct datasets from different geographic regions and patient populations and train models for three prediction tasks: 1-year mortality following an ICU stay, 30-day mortality following surgery, and 30-day complication following surgery. We run multiple commonly-used binary classification models including penalized logistic regression, random forest, and gradient boosted trees.

A framework for making predictive models useful in practice.

Objective: To analyze the impact of factors in healthcare delivery on the net benefit of triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month mortality.

Materials And Methods: We built a predictive model of 12-month mortality using electronic health record data and evaluated the impact of healthcare delivery factors on the net benefit of triggering an ACP workflow based on the models' predictions. Factors included nonclinical reasons that make ACP inappropriate: limited capacity for ACP, inability to follow up due to patient discharge, and availability of an outpatient workflow to follow up on missed cases.

Just-in-time postnatal education programees to improve newborn care practices: needs and opportunities in low-resource settings.

Worldwide, many newborns die in the first month of life, with most deaths happening in low/middle-income countries (LMICs). Families' use of evidence-based newborn care practices in the home and timely care-seeking for illness can save newborn lives. Postnatal education is an important investment to improve families' use of evidence-based newborn care practices, yet there are gaps in the literature on postnatal education programees that have been evaluated to date.

Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.

Objective: Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order.

Materials And Methods: 16,103 SARS-CoV-2 RT-PCR tests were performed on 15,807 patients at Stanford facilities between March 2 and April 11, 2020.

Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study.

Background: Pythia is an automated, clinically curated surgical data pipeline and repository housing all surgical patient electronic health record (EHR) data from a large, quaternary, multisite health institute for data science initiatives. In an effort to better identify high-risk surgical patients from complex data, a machine learning project trained on Pythia was built to predict postoperative complication risk.

Methods And Findings: A curated data repository of surgical outcomes was created using automated SQL and R code that extracted and processed patient clinical and surgical data across 37 million clinical encounters from the EHRs.