Customized Care: An intervention to Improve Communication and health outcomes in multimorbidity

Marsha N Wittink, Sule Yilmaz, Patrick Walsh, Ben Chapman, Paul Duberstein, Marsha N Wittink, Sule Yilmaz, Patrick Walsh, Ben Chapman, Paul Duberstein

Abstract

Introduction: Many primary care patients with multimorbidity (two or more chronic conditions) and depression or anxiety have day-to-day challenges that affect health outcomes, such as having financial or housing concerns, or dealing with social or emotional stressors. Yet, primary care providers (PCPs) are often unaware of patients' daily challenges coping with chronic disease. We developed Customized Care, an intervention, to address the barriers to effective communication about patient's day-to-day challenges.

Methods: In this report we describe the rationale and design of a randomized clinical pilot study to examine the effect of Customized Care on patient-PCP communication and patient health outcomes, including depression, anxiety and functional outcomes. Customized Care comprises two components: (1) a computer-based discussion prioritization tool (DPT) designed to empower patients to communicate their health related priorities; and (2) a customized question prompt list (QPL) tailored to these priorities. Primary care clinic patients and PCPs participated in the study, which consisted of in-person patient assessments, audio recording and transcription of the patient-PCP office visit, and follow-up patient assessments by phone.

Results: We describe study participant demographics and development of a coding manual to assess communication within the office visit. Participants were recruited from an urban primary care clinic. Sixty patients and 12 PCPs were enrolled over six months.

Conclusions: With better communication about everyday challenges, patients and PCPs can have more informed discussions about health care options that positively influence patient outcomes. We expect that Customized Care will improve patient-PCP communication about day-to-day challenges, which can lead to better health outcomes.

Keywords: Doctor-patient communication; Multimorbidity; Primary care; Randomized clinical trial.

Conflict of interest statement

There are no conflicts of interest to disclose. All study procedures were approved by the Research Subjects Review Board of the University of Rochester School of Medicine.

Figures

Fig. 1
Fig. 1
Conceptual model, pathways to improved communications and health outcomes.
Fig. 2
Fig. 2
Example of trade-off task shown in the DPT.
Fig. 3
Fig. 3
Example QPL.
Fig. 4
Fig. 4
Measurement time-points.

References

    1. Fortin M., Hudon C., Haggerty J., van den Akker M., Almirall J. Prevalence estimates of multimorbidity: a comparative study of two sources. BMC Health Serv. Res. 2010;10
    1. van den Akker M., Buntinx F., Metsemakers J.F.M., Roos S., Knottnerus J.A. Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J. Clin. Epidemiol. 1998;51(5):367–375.
    1. Blazer D.G., Kessler R.C., McGonagle K.A., Swartz M.S. The prevalence and distribution of major depression in a national community sample: the National Comorbidity Survey. Am. J. psychiatry. 1994;151(7):979–986.
    1. McGaw J.L. Whole patient care: reaching beyond traditional healthcare. Front. health Serv. Manag. 2008;25(2):39–42. discussion 43, 45-36.
    1. Tinetti M.E., Fried T.R., Boyd C.M. Designing health care for the most common chronic condition–multimorbidity. JAMA J. Am. Med. Assoc. 2012;307(23):2493–2494.
    1. Gijsen R., Hoeymans N., Schellevis F.G., Ruwaard D., Satariano W.A., van den Bos G.A. Causes and consequences of comorbidity: a review. J. Clin. Epidemiol. 2001;54(7):661–674.
    1. Neeleman J., Ormel J., Bijl R.V. The distribution of psychiatric and somatic III health: associations with personality and socioeconomic status. Psychosom. Med. 2001;63(2):239–247.
    1. Bayliss E.A., Steiner J.F., Fernald D.H., Crane L.A., Main D.S. Descriptions of barriers to self-care by persons with comorbid chronic diseases. Ann. Fam. Med. 2003;1(1):15–21.
    1. Bayliss E.A., Edwards A.E., Steiner J.F., Main D.S. Processes of care desired by elderly patients with multimorbidities. Fam. Pract. 2008;25(4):287–293.
    1. Martin L.R., Williams S.L., Haskard K.B., Dimatteo M.R. The challenge of patient adherence. Ther. Clin. risk Manag. 2005;1(3):189–199.
    1. Weiner S.J., Schwartz A. Contextual errors in medical decision making: overlooked and understudied. Acad. Med. 2016;91(5):657–662.
    1. Street R.L., Jr. How clinician-patient communication contributes to health improvement: modeling pathways from talk to outcome. Patient Educ. Couns. 2013;92(3):286–291.
    1. Street R.L., Jr., Makoul G., Arora N.K., Epstein R.M. How does communication heal? Pathways linking clinician-patient communication to health outcomes. Patient Educ. Couns. 2009;74(3):295–301.
    1. Bayliss E.A., Bosworth H.B., Noel P.H., Wolff J.L., Damush T.M., McIver L. Supporting self-management for patients with complex medical needs: recommendations of a working group. Chronic Illn. 2007;3(2):167–175.
    1. Jerant A.F., von Friederichs-Fitzwater M.M., Moore M. Patients' perceived barriers to active self-management of chronic conditions. Patient Educ. Couns. 2005;57(3):300–307.
    1. Zulman D.M., Kerr E.A., Hofer T.P., Heisler M., Zikmund-Fisher B.J. Patient-provider concordance in the prioritization of health conditions among hypertensive diabetes patients. J. general Intern. Med. 2010;25(5):408–414.
    1. Frosch D.L., May S.G., Rendle K.A., Tietbohl C., Elwyn G. Authoritarian physicians and patients' fear of being labeled 'difficult' among key obstacles to shared decision making. Health Aff. (Millwood). 2012;31(5):1030–1038.
    1. Wittink M.N., Barg F.K., Gallo J.J. Unwritten rules of talking to doctors about depression: integrating qualitative and quantitative methods. Ann. Fam. Med. 2006;4(4):302–309.
    1. Weiner S.J., Schwartz A., Weaver F. Contextual errors and failures in individualizing patient care: a multicenter study. Ann. Intern Med. 2010;153(2):69–75.
    1. RWJF . Robert Wood Johnson Foundation; 2011. Health Care's Blind Side: the Overlooked Connection between Social Needs and Good Health.
    1. Roter D., Hall J. Praeger Publishers; Westport, CT: 2005. Doctors Talking with Patients/Patients Talking with Doctors.
    1. Voigt I., Wrede J., Diederichs-Egidi H., Dierks M.L., Junius-Walker U. Priority setting in general practice: health priorities of older patients differ from treatment priorities of their physicians. Croat. Med. J. 2010;51(6):483–492.
    1. Marvel M.K., Epstein R.M., Flowers K., Beckman H.B. Soliciting the patient's agenda: have we improved? JAMA J. Am. Med. Assoc. 1999;281(3):283–287.
    1. (IOM) IoM . National Academy Press; Washington DC: 2001. Crossing the Quality Chasm: a new health system for the 21st century.
    1. Thaler R.H., Sunstein C.R. Penguin Books; New York: 2009. Nudge : Improving Decisions about Health, Wealth, and Happiness. Rev. and expanded ed.
    1. Kahneman D. first ed. Farrar, Straus and Giroux; New York: 2011. Thinking, Fast and Slow.
    1. Bertakis K.D., Azari R. Patient-centered care is associated with decreased health care utilization. J. Am. Board Fam. Med. JABFM. 2011;24(3):229–239.
    1. Wittink M.N., Cary M., Tenhave T., Baron J., Gallo J.J. Towards patient-centered care for depression: conjoint methods to tailor treatment based on preferences. Patient. 2010;3(3):145–157.
    1. Health Care's Blind Side: the Overlooked Connection between Social Needs and Good Health. Robert Wood Johnson Foundation; 2011.
    1. Dahan E. Medicine 2.0 World Congress on Social Media, Mobile Apps, Internet/Web 2.0. 2012. Enhancing in-clinic doctor-patient communication in real time through adaptive best-worst conjoint (ABC) analysis. (Boston, USA)
    1. E. Dahan, S. Lambrechts, R. Kaplan, C. Saigal. Treatment Preferecnes Derived Using Adaptive Best-Worst Conjoint (ABC) Analysis. Paper presented at: Society for Medical Decision Making 2012; Phoenix, AZ.
    1. Dahan E. 2012. Conjoint Analysis in Healthcare Conference March 21, 2012. (Orlando, Florida)
    1. Jaspers M.W.M., Steen T., van den Bos C., Geenen M. The think aloud method: a guide to user interface design. Int. J. Med. Inf. 2004;73(11–12):781–795.
    1. Yen P, Wantland D, Bakken S. Development of a customizable health it usability evaluation scale. Paper Presented at: AMIA Annual Symosium 2010.
    1. Charlson M.E., Pompei P., Ales K.L., MacKenzie C.R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 1987;40(5):373–383.
    1. Chew L.D., Bradley K.A., Boyko E.J. Brief questions to identify patients with inadequate health literacy. Fam. Med. 2004;36(8):588–594.
    1. Chew L.D., Griffin J.M., Partin M.R. Validation of screening questions for limited health literacy in a large VA outpatient population. J. Gen. Intern Med. 2008;23(5):561–566.
    1. Kroenke K., Strine T.W., Spitzer R.L., Williams J.B., Berry J.T., Mokdad A.H. The PHQ-8 as a measure of current depression in the general population. J. Affect Disord. 2009;114(1–3):163–173.
    1. Spitzer R.L., Kroenke K., Williams J.B., Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch. Intern Med. 2006;166(10):1092–1097.
    1. Ware J., Kosinski M., Keller S.D. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med. Care. 1996;34(3):220–233.
    1. Williams G.C., Deci E.L. Internalization of biopsychosocial values by medical students: a test of self-determination theory. J. Pers. Soc. Psychol. 1996;70(4):767–779.
    1. Williams G.C., Freedman Z.R., Deci E.L. Supporting autonomy to motivate patients with diabetes for glucose control. Diabetes Care. 1998;21(10):1644–1651.
    1. Wittink M.N., Walsh P., Yilmaz S., Chapman B., Duberstein P. University of Rochester School of Medicine; 2016. Assessing Patients' Disclosure of Day-to-day Health-related Challenges: Development of the Patients' Everyday Dilemmas Coding Scheme.
    1. Street R.L., Jr., Millay B. Analyzing patient participation in medical encounters. Health Commun. 2001;13(1):61–73.
    1. Williams G.C., Lynch M., Glasgow R.E. Computer-assisted intervention improves patient-centered diabetes care by increasing autonomy support. Health Psychol. official J. Div. Health Psychol. Am. Psychol. Assoc. 2007;26(6):728–734.
    1. Barr P.J., Thompson R., Walsh T., Grande S.W., Ozanne E.M., Elwyn G. The psychometric properties of CollaboRATE: a fast and frugal patient-reported measure of the shared decision-making process. J. Med. Internet Res. 2014;16(1):e2.
    1. Elwyn G., Barr P.J., Grande S.W., Thompson R., Walsh T., Ozanne E.M. Developing CollaboRATE: a fast and frugal patient-reported measure of shared decision making in clinical encounters. Patient Educ. Couns. 2013;93(1):102–107.
    1. Sirey J.A., Meyers B.S., Teresi J.A. The Cornell Service Index as a measure of health service use. Psychiatr. Serv. 2005;56(12):1564–1569.
    1. Lachin J.M. Statistical considerations in the intent-to-treat principle. Control Clin. Trials. 2000;21(3):167–189.
    1. Davidian M., Giltinan D.M. Nonlinear models for repeated measurement data: an overview and update. J. Agric. Biol. Environ. Statistics. 2003;8(4):387–419.
    1. Clever S.L., Ford D.E., Rubenstein L.V. Primary care patients' involvement in decision-making is associated with improvement in depression. Med. care. 2006;44(5):398–405.
    1. Coulter A., Collins A. The King's Fund; London: 2011. Making Shared Decision-making a Reality: No Decision about Me, without Me.
    1. Wittink M.N., Givens J.L., Knott K.A., Coyne J.C., Barg F.K. Negotiating depression treatment with older adults: primary care providers' perspectives. J. Ment. Health. 2011;20(5):429–437.
    1. Wittink M.N., Morales K.H., Cary M., Gallo J.J., Bartels S.J. Towards personalizing treatment for depression: developing treatment values markers. Patient. 2013;6(1):35–43.
    1. Fernandez Y-Garcia E., Duberstein P., Paterniti D.A., Cipri C.S., Kravitz R.L., Epstein R.M. Feeling labeled, judged, lectured, and rejected by family and friends over depression: cautionary results for primary care clinicians from a multi-centered, qualitative study. BMC Fam. Pract. 2012;13:64.
    1. Wittink M.N., Duberstein P., Lyness J.M. Oxford University Press; 2013. Late-life Deprssion in the Primary Care Setting: toward a Patient Centered Future.
    1. Poleshuck E., Wittink M., Crean H. Using patient engagement in the design and rationale of a trial for women with depression in obstetrics and gynecology practices. Contemp. Clin. Trials. 2015;43:83–92.
    1. Kravitz R.L., Franks P., Feldman M.D. Patient engagement programs for recognition and initial treatment of depression in primary care: a randomized trial. JAMA J. Am. Med. Assoc. 2013;310(17):1818–1828.
    1. Grant R.W., Uratsu C.S., Estacio K.R. Pre-Visit Prioritization for complex patients with diabetes: randomized trial design and implementation within an integrated health care system. Contemp. Clin. Trials. 2016;47:196–201.
    1. Kinnersley P., Edwards A., Hood K. Interventions before consultations to help patients address their information needs by encouraging question asking: systematic review. BMJ. 2008;337:a485.
    1. Weiner S.J. From research evidence to context: the challenge of individualising care. Equine Vet. J. 2006;38(3):195–196.

Source: PubMed

Sponsoren und Mitarbeiter

Krankheiten

Drogeninterventionen

3
Abonnieren