Patient Priority Care for Older Adults With Multiple Chronic Conditions

Overview

Healthcare for older adults with multiple chronic conditions (MCCs) is burdensome and of uncertain benefit, resulting in unwanted and unhelpful care. Patient Priorities Care (PPC) aligns care with patients' health priorities (i.e. the health outcomes most desired given the healthcare each is willing and able to receive). The aim of this project is to test, using a parallel group design involving 2 matched primary care sites, whether PPC decreases patient treatment burden and unwanted and unnecessary health care as well as assess what the value of this program is for patients.

Full Title of Study: “Patient Priority Care for Older Adults With Multiple Chronic Conditions Achieved Through Primary and Specialty Care Alignment”

Study Type

  • Study Type: Interventional
  • Study Design
    • Allocation: Non-Randomized
    • Intervention Model: Parallel Assignment
    • Primary Purpose: Treatment
    • Masking: Single (Outcomes Assessor)
  • Study Primary Completion Date: May 31, 2021

Detailed Description

Healthcare for older adults with multiple chronic conditions (MCCs) is burdensome and of uncertain benefit, resulting in unwanted and unhelpful care. Patient Priorities Care (PPC) is an approach that aligns care with patients' health priorities (i.e. the health outcomes most desired given the healthcare each is willing and able to receive). PPC offers the opportunity to increase value by improving both outputs (desired health outcomes) and inputs (healthcare preferences) for these major users of healthcare. We will employ a quasi-experimental, usual care (UC) group design, involving 2 primary care sites (1 PPC and 1 UC. Patients are assigned to intervention or usual care arms based on their primary care practice location. We will use analytic techniques (e.g., inverse propensity score weighting) designed to reduce selection bias and balance PPC and UC sites in terms of baseline characteristics. Data collection will occur through quantitative and qualitative interviews and health encounter information in the Electric Health Record(EHR). Patient Priorities Care requires the elicitation and documentation of patient health outcome goals and care preferences and the alignment of clinical care with goals and priorities to achieve patients' health outcome goals and reduce the burden of multi-morbidity. Participants will be enrolled in the Patient Priorities Care Program and speak with a trained health priorities facilitator to elicit their healthcare preferences and health outcome goals, which together constitute their health priorities. This information will be documented, entered into the EHR, and shared with the clinicians who will then use the Patient Priorities Care approach with patients to inform and guide treatment decisions. Patients will participate in the program and be followed for up to one year from the health priorities identification visit. To determine the value of PPC, comparable primary care sites within the Cleveland Clinic will be assigned to PPC or Usual care (UC). Clinicians and staff at the PPC site will be trained to identify and align decision-making with the health priorities of older adults with MCCs. Value will be compared using patient and provider-reported outcomes, healthcare utilization, and possibly costs at PPC and UC sites. The ultimate goal of our work is to implement and evaluate this approach to care for older adults with multiple chronic conditions that focuses on what matters most to them and is less fragmented and burdensome, resulting in better quality and outcomes at lower cost. This study will focus on evaluating practice change at test sites at the Cleveland Clinic.

Interventions

  • Behavioral: Patient Priorities Care
    • Patient Priorities Care (PPC) is an innovative approach to shared decision-making that draws from existing professional training. PPC requires the elicitation and documentation of patient health outcome goals and care preferences and the alignment of clinical care with health goals and healthcare preferences. This information will be collected and documented in the EHR by facilitators and shared with the clinicians who will then use the PPC approach with patients to inform and guide treatment decisions. The PCPs will be trained in decisional strategies that have been shown to help align care with patients’ health priorities. While encouraged to use these decisional strategies, PCPs will be free to make the recommendations they feel most appropriate for each patient. This intervention has been developed to be integrated seamlessly into usual care.

Arms, Groups and Cohorts

  • Experimental: Intervention (Implementing Patient Priorities Care)
    • Patient Priorities Care requires the elicitation and documentation of patient health outcome goals and care preferences and the alignment of clinical care with health goals and healthcare preferences (collectively referred to as health priorities). Participants will be contacted by a trained priorities facilitator in-person or over the phone to elicit their health priorities. This information will be documented in the PPC- GOALS AND PREFERENCES form in the EHR and shared with the clinicians who will then use the Patient Priorities Care approach with patients to inform and guide treatment decisions.
  • No Intervention: Usual Care (Not implementing PPC)
    • Patients will receive routine clinical care.

Clinical Trial Outcome Measures

Primary Measures

  • Treatment burden
    • Time Frame: from baseline to follow-up at 8-9 months
    • Change in patient score on ‘Treatment Burden Questionnaire’ (TBQ, score range 0-150, Cronbach’s alpha=0.90)
  • Achievement of desired activities
    • Time Frame: from baseline to follow-up at 8-9 months
    • Change in patient score on PROMIS Ability to Participate in Social Roles and Activities Shot Form 6a (score range 6-30; Cronbach’s alpha = 0.98)
  • Health care utilization defined by healthcare contact days
    • Time Frame: from 3 months prior to 12 months following baseline interview
    • Number of health care contact days defined as number of ED visits, days in hospital +.5*number of outpatient encounters for procedures, tests, healthcare visits

Secondary Measures

  • Shared decision making and goal ascertainment
    • Time Frame: at 8-9 months follow-up
    • Change in patient score on CollaboRATE tool (score 0-100, Cronbach’s alpha=0.89) from baseline to follow-up up at 8-9 months and response to Cleveland Clinic ACO survey item “When starting a new medication, did your provider ask what you thought was best for you?”
  • Alignment of healthcare with patient preferences (coded based on review of EHR)
    • Time Frame: Variable will be coded based on review of EHR covering the 12 months post baseline follow-up.
    • Dichotomous variable indicating whether medications or self-management tasks were added or stopped per patient preference. Data will be abstracted using a data dictionary which guided abstraction in pilot studies.

Participating in This Clinical Trial

Inclusion Criteria

1. Age 66 and older 2. In the Cleveland Clinic patient population 3. In the clinician practices selected as intervention or usual care practice sites 4. Clinically identified by: Those who meet any of several criteria i. 3 chronic conditions (See appendix 0 for the complete list) ii. 10 medications iii. >2 ED visits over the past year iv. >1 hospitalization (or >10 days in hospital) v. receive any care coordination services vi. 2 specialists over past year Exclusion Criteria:

1. In hospice or meeting hospice criteria for any condition 2. Advanced dementia or moderate to profound intellectual disabilities 3. Not English speaking 4. Nursing home resident

Gender Eligibility: All

Minimum Age: 66 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • The Cleveland Clinic
  • Collaborator
    • Yale University
  • Provider of Information About this Clinical Study
    • Principal Investigator: Ardeshir Hashmi, Center Director, Geriatrics – The Cleveland Clinic
  • Overall Official(s)
    • Ardeshir Hashmi, MD, Principal Investigator, The Cleveland Clinic

References

Boyd C, Smith CD, Masoudi FA, Blaum CS, Dodson JA, Green AR, Kelley A, Matlock D, Ouellet J, Rich MW, Schoenborn NL, Tinetti ME. Decision Making for Older Adults With Multiple Chronic Conditions: Executive Summary for the American Geriatrics Society Guiding Principles on the Care of Older Adults With Multimorbidity. J Am Geriatr Soc. 2019 Apr;67(4):665-673. doi: 10.1111/jgs.15809. Epub 2019 Mar 10.

Tinetti ME, Naik AD, Dindo L, Costello DM, Esterson J, Geda M, Rosen J, Hernandez-Bigos K, Smith CD, Ouellet GM, Kang G, Lee Y, Blaum C. Association of Patient Priorities-Aligned Decision-Making With Patient Outcomes and Ambulatory Health Care Burden Among Older Adults With Multiple Chronic Conditions: A Nonrandomized Clinical Trial. JAMA Intern Med. 2019 Oct 7. doi: 10.1001/jamainternmed.2019.4235. [Epub ahead of print]

Z. Zhao, "Using matching to estimate treatment effects: Data requirements, matching metrics, and Monte Carlo evidence," in Review of Economics and Statistics, 2004, doi: 10.1162/003465304323023705.

D. B. Rubin, "Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies," J. Am. Stat. Assoc., 1979, doi: 10.1080/01621459.1979.10482513.

Tran VT, Harrington M, Montori VM, Barnes C, Wicks P, Ravaud P. Adaptation and validation of the Treatment Burden Questionnaire (TBQ) in English using an internet platform. BMC Med. 2014 Jul 2;12:109. doi: 10.1186/1741-7015-12-109.

Hahn EA, Kallen MA, Jensen RE, Potosky AL, Moinpour CM, Ramirez M, Cella D, Teresi JA. Measuring social function in diverse cancer populations: Evaluation of measurement equivalence of the Patient Reported Outcomes Measurement Information System(®) (PROMIS(®)) Ability to Participate in Social Roles and Activities short form. Psychol Test Assess Model. 2016 Jun 27;58(2):403-421.

Groff AC, Colla CH, Lee TH. Days Spent at Home – A Patient-Centered Goal and Outcome. N Engl J Med. 2016 Oct 27;375(17):1610-1612.

Russell LB, Ibuka Y, Carr D. How Much Time Do Patients Spend on Outpatient Visits?: The American Time Use Survey. Patient. 2008 Jul 1;1(3):211-22. doi: 10.2165/1312067-200801030-00008.

Forcino RC, Barr PJ, O'Malley AJ, Arend R, Castaldo MG, Ozanne EM, Percac-Lima S, Stults CD, Tai-Seale M, Thompson R, Elwyn G. Using CollaboRATE, a brief patient-reported measure of shared decision making: Results from three clinical settings in the United States. Health Expect. 2018 Feb;21(1):82-89. doi: 10.1111/hex.12588. Epub 2017 Jul 5.

Blaum CS, Rosen J, Naik AD, Smith CD, Dindo L, Vo L, Hernandez-Bigos K, Esterson J, Geda M, Ferris R, Costello D, Acampora D, Meehan T, Tinetti ME. Feasibility of Implementing Patient Priorities Care for Older Adults with Multiple Chronic Conditions. J Am Geriatr Soc. 2018 Oct;66(10):2009-2016. doi: 10.1111/jgs.15465. Epub 2018 Oct 3.

Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015 Dec 10;34(28):3661-79. doi: 10.1002/sim.6607. Epub 2015 Aug 3. Review.

D. B. Rubin, "Using propensity scores to help design observational studies: Application to the tobacco litigation," Matched Sampl. Causal Eff., pp. 365-382, 2006, doi:

Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci. 2010 Feb 1;25(1):1-21.

Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res. 2011 May;46(3):399-424. Epub 2011 Jun 8.

D. Rubin, Multiple Imputation for Nonresponse in Surveys. Wiley, 1987.

J. Cohen, Statistical Power Analysis for the Behavioral Sciences. Hillsdale, New Jersey, 1988.

Tinetti M, Dindo L, Smith CD, Blaum C, Costello D, Ouellet G, Rosen J, Hernandez-Bigos K, Geda M, Naik A. Challenges and strategies in patients' health priorities-aligned decision-making for older adults with multiple chronic conditions. PLoS One. 2019 Jun 10;14(6):e0218249. doi: 10.1371/journal.pone.0218249. eCollection 2019.

Clinical trials entries are delivered from the US National Institutes of Health and are not reviewed separately by this site. Please see the identifier information above for retrieving further details from the government database.

At TrialBulletin.com, we keep tabs on over 200,000 clinical trials in the US and abroad, using medical data supplied directly by the US National Institutes of Health. Please see the About and Contact page for details.