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mHOMR: the acceptability of an automated mortality prediction model for timely identification of patients for palliative care
  1. Stephanie Saunders1,
  2. James Downar2,3,4,5,
  3. Saranjah Subramaniam6,
  4. Gaya Embuldeniya7,8,
  5. Carl van Walraven3,5,9,
  6. Pete Wegier6,8,10
  1. 1 Department of Rehabilitation Sciences, McMaster University, Hamilton, Ontario, Canada
  2. 2 Division of Palliative Care, The Ottawa Hospital, Ottawa, Ontario, Canada
  3. 3 Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
  4. 4 Bruyère Research Institute, Ottawa, Ontario, Canada
  5. 5 Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
  6. 6 Humber River Hospital, Toronto, Ontario, Canada
  7. 7 Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
  8. 8 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
  9. 9 Department of Epidemiology & Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
  10. 10 Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
  1. Correspondence to Dr Pete Wegier, Humber River Hospital, Toronto, ON M3M 0B2, Canada; pwegier{at}hrh.ca

https://doi.org/10.1136/bmjqs-2020-012461

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    Introduction

    Patients with non-cancer serious illnesses are under-recognised and receive palliative care only in the final weeks of life, if at all.1 The modified Hospitalised-patient One-year Mortality Risk (mHOMR) tool is a computer-based mortality prediction tool that accurately identifies patients at risk of 1-year mortality and is a feasible alternative to healthcare provider (HCP)-dependent models.2 Briefly, the tool uses data from the electronic health record to calculate an mHOMR score for each new hospital admission. The alert only notifies the lead physician, suggesting they refer the patient topalliative care and does not provide the actual score.2 In this study, we sought the perspectives of patients, family members, and HCPs to identify acceptability of mHOMR as a mortality risk tool. Together, these two studies represent the feasibility and acceptability components of the implementation outcomes (IO) framework.3

    Methods

    Previously we reported the development and feasibility of mHOMR (see Wegier et al 2 for more details). Alongside the feasibility study2 we collected qualitative data from November 2016 to May 2017 pre-implementation and from June to October 2017 post-implementation at two quaternary hospitals in Toronto, Canada. We used a postpositivist, qualitative content methodology4 and consecutively recruited: (1) English-speaking patients admitted to a medicosurgical ward with an mHOMR score >0.21 (ie, >21% risk of death in 12 months) and (2) HCPs who admitted patients with an mHOMR score >0.21 or were involved in advance care planning or goals of care (GOC) discussions with these patients. Substitute decision makers were recruited if a patient could not consent. In-person interviews with patients and caregivers and phone interviews with HCPs were …

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    Footnotes

    • Twitter @petewegier

    • Contributors JD conceived the study and developed the protocol. PW, SSa and SSu 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 was 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 through a Phoenix Fellowship.

    • Competing interests None declared.

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

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