Automated Identification of Patients with Advanced Illness.

Early identification of advanced illness patients within an inpatient population is essential in order to establish the patient's goals of care. Having goals of care conversations enables hospital patients to dictate a plan for care in concordance with their values and wishes. These conversations allow a patient to maintain some control, rather than be subjected to a default care process that may not be desired and may not provide benefit. In this study the performance of two approaches which identify advanced illness patients within an inpatient population were evaluated: LACE (a rule-based approach that uses L - Length of stay, A- Acuity of Admission, C- Co-morbidities, E- Emergency room visits), and a novel approach: Hospital Impairment Score (HIS). The Hospital impairment score is derived by leveraging both rule-based insights and a novel machine learning algorithm. It was identified that HIS significantly outperformed the LACE score, the current model being used in production at Northwell Health. Furthermore, we describe how the HIS model was piloted at a single hospital, was launched into production, and is being successfully used by clinicians at that hospital.

Changing the Care Paradigm for Patients: Advanced Illness Beds Care Model.

BACKGROUND: Over 90 million Americans suffer from advanced illness (AI) and spend their last days of life in critical care units receiving costly, unwanted, aggressive medical care. OBJECTIVE: Evaluate the impact of a specialized care model in medical/surgical units for hospitalized geriatric patients and patients with complex care requirements where designated AI beds align care with patient's wishes/goals, minimize aggressive interventions, and influence efficient resource utilization. DESIGN: US based multi-facility retrospective, longitudinal descriptive study of screened positive AI patients in AI Beds (N = 1,237) from 3 facilities from 2015 to 2017. RESULTS: Patient outcomes included 60% referrals to AI beds from ICU, a decrease of 39-49% in average ICU LOS, a 23% reduction of AI bed patient expirations, 9.0% referrals to hospice, and projected cost savings of $4,361.66/patient, US dollars. CONCLUSION: Allocating AI beds to deliver care to AI patients resulted in a decreased cost of care by reducing overall hospital LOS, mortality, and efficient use of both critical care and hospital resources.