Improving time to palliative care review with predictive modeling in an inpatient adult population: study protocol for a stepped-wedge, pragmatic randomized controlled trial

Patrick M Wilson, Lindsey M Philpot, Priya Ramar, Curtis B Storlie, Jacob Strand, Alisha A Morgan, Shusaku W Asai, Jon O Ebbert, Vitaly D Herasevich, Jalal Soleimani, Brian W Pickering, Patrick M Wilson, Lindsey M Philpot, Priya Ramar, Curtis B Storlie, Jacob Strand, Alisha A Morgan, Shusaku W Asai, Jon O Ebbert, Vitaly D Herasevich, Jalal Soleimani, Brian W Pickering

Abstract

Background: Palliative care is a medical specialty centered on improving the quality of life (QOL) of patients with complex or life-threatening illnesses. The need for palliative care is increasing and with that the rigorous testing of triage tools that can be used quickly and reliably to identify patients that may benefit from palliative care.

Methods: To that aim, we will conduct a two-armed stepped-wedge cluster randomized trial rolled out to two inpatient hospitals to evaluate whether a machine learning algorithm accurately identifies patients who may benefit from a comprehensive review by a palliative care specialist and decreases time to receiving a palliative care consult in hospital. This is a single-center study which will be conducted from August 2019 to November 2020 at Saint Mary's Hospital & Methodist Hospital both within Mayo Clinic Rochester in Minnesota. Clusters will be nursing units which will be chosen to be a mix of complex patients from Cardiology, Critical Care, and Oncology and had previously established relationships with palliative medicine. The stepped wedge design will have 12 units allocated to a design matrix of 5 treatment wedges. Each wedge will last 75 days resulting in a study period of 12 months of recruitment unless otherwise specified. Data will be analyzed with Bayesian hierarchical models with credible intervals denoting statistical significance.

Discussion: This intervention offers a pragmatic approach to delivering specialty palliative care to hospital patients in need using machine learning, thereby leading to high value care and improved outcomes. It is not enough for AI to be utilized by simply publishing research showing predictive performance; clinical trials demonstrating better outcomes are critically needed. Furthermore, the deployment of an AI algorithm is a complex process that requires multiple teams with varying skill sets. To evaluate a deployed AI, a pragmatic clinical trial can accommodate the difficulties of clinical practice while retaining scientific rigor.

Trial registration: ClinicalTrials.gov NCT03976297 . Registered on 6 June 2019, prior to trial start.

Keywords: AI; Artificial intelligence; Electronic medical record; Palliative care; Pragmatic clinical trials; Stepped wedge trials.

Conflict of interest statement

The authors have no conflicts of interest or competing interests to disclose.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Screenshot of the Control Tower user interface
Fig. 2
Fig. 2
Stepped wedge design
Fig. 3
Fig. 3
Stepped wedge design power curves

References

    1. WHO definition of palliative care. World Health Organization website. . Accessed 1 May 2020.
    1. Gomes B, Calanzani N, Gysels M, Hall S, Higginson IJ. Heterogeneity and changes in preferences for dying at home: a systematic review. BMC Palliat Care. 2013;12(1). 10.1186/1472-684X-12-7.
    1. QuickStats: percentage distribution of deaths, by place of death — United States, 2000–2014. MMWR Morb Mortal Wkly Rep. 2016;65:357. 10.15585/mmwr.6513a6.
    1. Strand JJ, Kamdar MM, Carey EC. Top 10 things palliative care clinicians wished everyone knew about palliative care. Mayo Clinic Proc. 88(8):859–65.
    1. Temel JS, Greer JA, Muzikansky A, Gallagher ER, Admane S, Jackson VA, Dahlin CM, Blinderman CD, Jacobsen J, Pirl WF, Billings JA, Lynch TJ. Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med. 2010;363(8):733–742. doi: 10.1056/NEJMoa1000678.
    1. Greer JA, Pirl WF, Jackson VA, Muzikansky A, Lennes IT, Heist RS, Gallagher ER, Temel JS. Effect of early palliative care on chemotherapy use and end-of-life care in patients with metastatic non-small-cell lung cancer. J Clin Oncol. 2012;30(4):394–400. doi: 10.1200/JCO.2011.35.7996.
    1. Hui D, Kim SH, Roquemore J, Dev R, Chisholm G, Bruera E. Impact of timing and setting of palliative care referral on quality of end-of-life care in cancer patients. Cancer. 2014;120(11):1743–1749. doi: 10.1002/cncr.28628.
    1. Earle CC, Park ER, Lai B, Weeks JC, Ayanian JZ, Block S. Identifying potential indicators of the quality of end-of-life cancer care from administrative data. J Clin Oncol. 2003;21(6):1133–1138. doi: 10.1200/JCO.2003.03.059.
    1. Zhang B, Wright AA, Huskamp HA, Nilsson ME, Maciejewski ML, Earle CC, Block SD, Maciejewski PK, Prigerson HG. Health care costs in the last week of life: associations with end-of-life conversations. Arch Intern Med. 2009;169(5):480–488. doi: 10.1001/archinternmed.2008.587.
    1. Chi S, Buettner B, Ma J, Pollard K, Muir M, Kolekar C, Al-Hammadi N, Kollef M, Dans M. Early palliative care consultation in the medical ICU: a cluster randomized crossover trial. Crit Care Med. 2019;47(1):17. doi: 10.1097/01.ccm.0000550826.30295.06.
    1. Earle CC, Landrum MB, Souza JM, Neville BA, Weeks JC, Ayanian JZ. Aggressiveness of cancer care near the end of life: is it a quality-of-care issue? J Clin Oncol. 2008;26(23):3860–3866. doi: 10.1200/JCO.2007.15.8253.
    1. Cheng WW, Willey J, Palmer JL, Zhang T, Bruera E. Interval between palliative care referral and death among patients treated at a comprehensive cancer center. J Palliat Med. 2005;8(5):1025–1032. doi: 10.1089/jpm.2005.8.1025.
    1. Pirovano M, Maltoni M, Nanni O, Marinari M, Indelli M, Zaninetta G, Petrella V, Barni S, Zecca E, Scarpi E, Labianca R, Amadori D, Luporini G. A new palliative prognostic score: a first step for the staging of terminally ill cancer patients. J Pain Symptom Manage. 1999;17(4):231–239. doi: 10.1016/S0885-3924(98)00145-6.
    1. Maltoni M, Nanni O, Pirovano M, Scarpi E, Indelli M, Martini C, Monti M, Arnoldi E, Piva L, Ravaioli A, Cruciani G, Labianca R, Amadori D. Successful validation of the palliative prognostic score in terminally ill cancer patients. J Pain Symptom Manage. 1999;17(4):240–247. doi: 10.1016/S0885-3924(98)00146-8.
    1. Romero-Brufau S, Whitford D, Whitford KJ, Manning DM, Huddleston JM. Identifying patients at risk of inhospital death or hospice transfer for early goals of care discussions in a US referral center: the HELPS model derived from retrospective data. BMJ Open. 2018;8(1):e015550. doi: 10.1136/bmjopen-2016-015550.
    1. Meffert C, Rücker G, Hatami I, Becker G. Identification of hospital patients in need of palliative care--a predictive score. BMC Palliat Care. 2016;15:21. doi: 10.1186/s12904-016-0094-7.
    1. Fischer SM, Gozansky WS, Sauaia A, Min S-J, Kutner JS, Kramer A. A practical tool to identify patients who may benefit from a palliative approach: the CARING criteria. J Pain Symptom Manage. 2006;31(4):285–292. doi: 10.1016/j.jpainsymman.2005.08.012.
    1. Anderson F, Downing GM, Hill J, Casorso L, Lerch N. Palliative Performance Scale (PPS): a new tool. J Palliat Care. 1996;12(1):5–11. doi: 10.1177/082585979601200102.
    1. Villa G, De Gaudio AR, Falsini S, Lanini I, Curtis JR. Development of END-of-Life ScorING-System to identify critically ill patients after initial critical care who are highly likely to die: a pilot study. Minerva Anestesiol. 2015;81(12):1318–1328.
    1. Jung K, Sudat SEK, Kwon N, Stewart WF, Shah NH. Predicting need for advanced illness or palliative care in a primary care population using electronic health record data. J Biomed Inform. 2019;92:103115. doi: 10.1016/j.jbi.2019.103115.
    1. Avati A, Jung K, Harman S, Downing L, Ng A, Shah NH. Improving palliative care with deep learning. BMC Med Inform Decis Mak. 2018;18(Suppl 4):122. doi: 10.1186/s12911-018-0677-8.
    1. Spiegelhalter D. Should we trust algorithms? Harvard Data Sci Rev. 2020;2(1). 10.1162/99608f92.cb91a35a.
    1. Courtright KR, Chivers C, Becker M, Regli SH, Pepper LC, Draugelis ME, O’Connor NR. Electronic health record mortality prediction model for targeted palliative care among hospitalized medical patients: a pilot quasi-experimental study. J Gen Intern Med. 2019;34(9):1841–1847. doi: 10.1007/s11606-019-05169-2.
    1. Murphree DH, Wilson PM, Asai SW, Quest DJ, Lin Y, Mukherjee P, Chhugani N, Strand JJ, Demuth G, Mead D, Wright B, Harrison A, Soleimani J, Herasevich V, Pickering BW, Storlie CB. Improving the delivery of palliative care through predictive modeling and healthcare informatics. J Am Med Inform Assoc. 2020;2021:ocaa211. doi: 10.1093/jamia/ocaa211.
    1. Landau S, Stahl D. Sample size and power calculations for medical studies by simulation when closed form expressions are not available. Stat Methods Med Res. 2013;22(3):324–345. doi: 10.1177/0962280212439578.
    1. Pasterkamp G, Hoefer I, Prakken B. Lost in the citation valley. Nat Biotechnol. 2016;34(10):1016–1018. doi: 10.1038/nbt.3691.
    1. Bennett LM, Gadlin H. Collaboration and team science: from theory to practice. J Investig Med. 2012;60(5):768–775. doi: 10.2310/JIM.0b013e318250871d.

Source: PubMed

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