Patient Priority Care for Older Adults With Multiple Chronic Conditions

Multiple Chronic Conditions Clinical Trial

— PPC-CCF  

Official title:

Patient Priority Care for Older Adults With Multiple Chronic Conditions Achieved Through Primary and Specialty Care Alignment

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04510948
• Other study ID #: 20-555
• Status: Active, not recruiting
• Phase: N/A
• Start date: August 14, 2020
• Est. completion date: June 30, 2023

Study information

• Verified date: January 2023
• Source: The Cleveland Clinic
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 250
• Est. completion date: June 30, 2023
• Est. primary completion date: January 15, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 66 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Disease
• Multiple Chronic Conditions

Intervention

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.

Locations

Country	Name	City	State
United States	Cleveland Clinic Lakewood Family Health Center	Lakewood	Ohio

Sponsors (3)

Lead Sponsor	Collaborator
The Cleveland Clinic	Donaghue Medical Research Foundation, Yale University

Country where clinical trial is conducted

United States, 

References & Publications (17)

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. — View Citation

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. doi: 10.1080/00273171.2011.568786. Epub 2011 Jun 8. — View Citation

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. — View Citation

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. — View Citation

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.

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:

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

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. — View Citation

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. doi: 10.1056/NEJMp1607206. No abstract available. — View Citation

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(R) (PROMIS(R)) Ability to Participate in Social Roles and Activities short form. Psychol Test Assess Model. 2016 Jun 27;58(2):403-421. — View Citation

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

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. — View Citation

Stuart EA. Matching methods for causal inference: A review and a look forward. Stat Sci. 2010 Feb 1;25(1):1-21. doi: 10.1214/09-STS313. — View Citation

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. — View Citation

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;179(12):1688-97. doi: 10.1001/jamainternmed.2019.4235. Online ahead of print. — View Citation

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. — View Citation

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.

* Note: There are 17 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Treatment burden	Change in patient score on 'Treatment Burden Questionnaire' (TBQ, score range 0-150, Cronbach's alpha=0.90)	from baseline to follow-up at 8-9 months
Primary	Achievement of desired activities	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)	from baseline to follow-up at 8-9 months
Primary	Health care utilization defined by healthcare contact days	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	from 3 months prior to 12 months following baseline interview
Secondary	Shared decision making and goal ascertainment	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?"	at 8-9 months follow-up
Secondary	Alignment of healthcare with patient preferences (coded based on review of EHR)	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.	Variable will be coded based on review of EHR covering the 12 months post baseline follow-up.

External Links

•   Patient Priorities Care helps patients and clinicians focus all decision-making and healthcare on what matters most: patients' own health priorities. It was developed by clinicians, patients, caregivers, health system leaders, and payers.