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Implementing automated prognostic models to inform palliative care: more than just the algorithm
  1. Erin M Bange1,2,
  2. Katherine R Courtright1,3,
  3. Ravi B Parikh1,2,3,4,5
  1. 1 Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  2. 2 Penn Center for Cancer Care Innovation, Abramson Cancer Center, Philadelphia, Pennsylvania, USA
  3. 3 Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  4. 4 Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  5. 5 Corporal Michael J Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
  1. Correspondence to Dr Ravi B Parikh, University of Pennsylvania, Philadelphia, PA 19104, USA; Ravi.Parikh{at}

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Palliative care is associated with improved patient-centred and caregiver-centred outcomes, higher-quality end-of-life care, and decreased healthcare use among patients with serious illness.1–3 The Centre to Advance Palliative Care has established a set of recommended clinical criteria (or ‘triggers’), including a projected survival of less than 1 year,4 to help clinicians identify patients likely to benefit from palliative care. Nevertheless, referrals often occur within the last 3 months of life5 due in part to clinician overestimation of prognosis.6 A growing number of automated predictive models leverage vast data in the electronic medical record (EMR) to accurately predict short-term mortality risk in real time and can be paired with systems to prompt clinicians to refer to palliative care.7–12 These models hold great promise to overcome the many clinician-level and system-level barriers to improving access to timely palliative care. First, mortality risk prediction algorithms have been shown to outperform clinician prognostic assessment, and clinician–machine collaboration may even outperform both.13 Second, algorithm-based ‘nudges’ that systematically provide prognostic information could address many cognitive biases, including status quo bias and optimism bias,14 15 that make clinicians less apt to identify patients who may benefit from palliative care. Indeed, such models have been shown to improve the frequency of palliative care delivery and patient outcomes in the hospital and clinic settings.9 16 17 With that said, successful implementation of automated prognostic models into routine clinical care at scale requires clinician and patient engagement and support.

In this issue of BMJ Quality & Safety, Saunders and colleagues report on the acceptability of using the EMR-based Modified Hospitalised-Patient One-Year Mortality Risk (mHOMR) score to alert clinicians to individual patients with a >21% risk of dying within 12 months. The goal of the clinician notification of an elevated risk score was to prompt …

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