Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models

BACKGROUND

Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance.

OBJECTIVE

We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system.

MATERIALS & METHODS

We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance.

RESULTS

Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85.

DISCUSSION

We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.

Undergoing transformation to the patient centered medical home in safety net health centers: perspectives from the front lines

OBJECTIVES

Safety net health centers (SNHCs), which include federally qualified health centers (FQHCs) provide primary care for underserved, minority and low income patients. SNHCs across the country are in the process of adopting the patient centered medical home (PCMH) model, based on promising early implementation data from demonstration projects. However, previous demonstration projects have not focused on the safety net and we know little about PCMH transformation in SNHCs.

DESIGN

This qualitative study characterizes early PCMH adoption experiences at SNHCs.

SETTING AND PARTICIPANTS

We interviewed 98 staff (administrators, providers, and clinical staff) at 20 of 65 SNHCs, from five states, who were participating in the first of a five-year PCMH collaborative, the Safety Net Medical Home Initiative.

MAIN MEASURES

We conducted 30-45 minute, semi-structured telephone interviews. Interview questions addressed benefits anticipated, obstacles encountered, and lessons learned in transition to PCMH.

RESULTS

Anticipated benefits for participating in the PCMH included improved staff satisfaction and patient care and outcomes. Obstacles included staff resistance and lack of financial support for PCMH functions. Lessons learned included involving a range of staff, anticipating resistance, and using data as frequent feedback.

CONCLUSIONS

SNHCs encounter unique challenges to PCMH implementation, including staff turnover and providing care for patients with complex needs. Staff resistance and turnover may be ameliorated through improved health care delivery strategies associated with the PCMH. Creating predictable and continuous funding streams may be more fundamental challenges to PCMH transformation.