mHOMR: a feasibility study of an automated system for identifying inpatients having an elevated risk of 1-year mortality

BMJ Qual Saf. 2019 Dec;28(12):971-979. doi: 10.1136/bmjqs-2018-009285. Epub 2019 Jun 28.

Abstract

Objective: The need for clinical staff to reliably identify patients with a shortened life expectancy is an obstacle to improving palliative and end-of-life care. We developed and evaluated the feasibility of an automated tool to identify patients with a high risk of death in the next year to prompt treating physicians to consider a palliative approach and reduce the identification burden faced by clinical staff.

Methods: Two-phase feasibility study conducted at two quaternary healthcare facilities in Toronto, Canada. We modified the Hospitalised-patient One-year Mortality Risk (HOMR) score, which identifies patients having an elevated 1-year mortality risk, to use only data available at the time of admission. An application prompted the admitting team when patients had an elevated mortality risk and suggested a palliative approach. The incidences of goals of care discussions and/or palliative care consultation were abstracted from medical records.

Results: Our model (C-statistic=0.89) was found to be similarly accurate to the original HOMR score and identified 15.8% and 12.2% of admitted patients at Sites 1 and 2, respectively. Of 400 patients included, the most common indications for admission included a frailty condition (219, 55%), chronic organ failure (91, 23%) and cancer (78, 20%). At Site 1 (integrated notification), patients with the notification were significantly more likely to have a discussion about goals of care and/or palliative care consultation (35% vs 20%, p = 0.016). At Site 2 (electronic mail), there was no significant difference (45% vs 53%, p = 0.322).

Conclusions: Our application is an accurate, feasible and timely identification tool for patients at elevated risk of death in the next year and may be effective for improving palliative and end-of-life care.

Keywords: decision support, computerized; healthcare quality improvement; trigger tools.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Electronic Data Processing
  • Electronic Health Records
  • Feasibility Studies
  • Female
  • Hospitals
  • Humans
  • Inpatients
  • Male
  • Mortality*
  • Ontario / epidemiology
  • Risk Assessment / methods*