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mHOMR: a feasibility study of an automated system for identifying inpatients having an elevated risk of 1-year mortality
  1. Pete Wegier1,2,3,
  2. Ellen Koo4,
  3. Shahin Ansari5,
  4. Daniel Kobewka6,7,8,
  5. Erin O'Connor9,10,11,
  6. Peter Wu12,
  7. Leah Steinberg1,3,
  8. Chaim Bell13,14,
  9. Tara Walton15,
  10. Carl van Walraven7,8,16,
  11. Gayathri Embuldeniya4,14,
  12. Judy Costello17,18,
  13. James Downar7,8,19
  1. 1 Temmy Latner Centre for Palliative Care, Sinai Health System, Toronto, Ontario, Canada
  2. 2 Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
  3. 3 Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
  4. 4 Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
  5. 5 Department of Decision Support, University Health Network, Toronto, Ontario, Canada
  6. 6 Department of Medicine, The Ottawa Hospital, Ottawa, Ontario, Canada
  7. 7 Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
  8. 8 Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada
  9. 9 Department of Emergency Medicine, University Health Network, Toronto, Ontario, Canada
  10. 10 Division of Palliative Medicine, University Health Network, Toronto, Ontario, Canada
  11. 11 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
  12. 12 Department of Medicine, University Health Network, Toronto, Ontario, Canada
  13. 13 Department of Medicine, Sinai Health System, Toronto, Ontario, Canada
  14. 14 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
  15. 15 Ontario Palliative Care Network, Toronto, Ontario, Canada
  16. 16 Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
  17. 17 Department of Medical Oncology and Hematology, University Health Network, Toronto, Ontario, Canada
  18. 18 Lawrence S Bloomberg Faculty of Nursing, University of Toronto, Toronto, Ontario, Canada
  19. 19 Bruyère Research Institute, Ottawa, Ontario, Canada
  1. Correspondence to Dr Pete Wegier, Sinai Health System, Toronto, Ontario, Canada; pete.wegier{at}sinaihealthsystem.ca; Dr James Downar, Bruyère Research Institute, Ottawa, Ontario, Canada; jdownar{at}toh.ca

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.

  • trigger tools
  • decision support, computerized
  • healthcare quality improvement

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Footnotes

  • Contributors JD, SA, DMK and CvW conceived the study and developed the protocol. PW and JD led the drafting of the manuscript. All authors contributed to data collection and/or analysis and interpretation, revising the manuscript and approved the final version submitted for publication.

  • Funding This research is funded by Canadian Frailty Network (Technology Evaluation in the Elderly Network), which is supported by the Government of Canada through the Networks of Centres of Excellence (NCE) programme. This project was also supported financially by the Temmy Latner Centre for Palliative Care and the Toronto General/Toronto Western Foundation, and received in-kind support from the Ottawa Hospital Research Institute. JD received support for this project from the Associated Medical Services, Incorporated through a Phoenix Fellowship.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement No data are available.

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