Improving patient-provider communication about chronic pain: development and feasibility testing of a shared decision-making tool

Nananda Col, Stephen Hull, Vicky Springmann, Long Ngo, Ernie Merritt, Susan Gold, Michael Sprintz, Noel Genova, Noah Nesin, Brenda Tierman, Frank Sanfilippo, Richard Entel, Lori Pbert, Nananda Col, Stephen Hull, Vicky Springmann, Long Ngo, Ernie Merritt, Susan Gold, Michael Sprintz, Noel Genova, Noah Nesin, Brenda Tierman, Frank Sanfilippo, Richard Entel, Lori Pbert

Abstract

Background: Chronic pain has emerged as a disease in itself, affecting a growing number of people. Effective patient-provider communication is central to good pain management because pain can only be understood from the patient's perspective. We aimed to develop a user-centered tool to improve patient-provider communication about chronic pain and assess its feasibility in real-world settings in preparation for further evaluation and distribution.

Methods: To identify and prioritize patient treatment goals for chronic pain, strategies to improve patient-provider communication about chronic pain, and facilitate implementation of the tool, we conducted nominal group technique meetings and card sorting with patients with chronic pain and experienced providers (n = 12). These findings informed the design of the PainAPP tool. Usability and beta-testing with patients (n = 38) and their providers refined the tool and assessed its feasibility, acceptability, and preliminary impact.

Results: Formative work revealed that patients felt neither respected nor trusted by their providers and focused on transforming providers' negative attitudes towards them, whereas providers focused on gathering patient information. PainAPP incorporated areas prioritized by patients and providers: assessing patient treatment goals and preferences, functional abilities and pain, and providing patients tailored education and an overall summary that patients can share with providers. Beta-testing involved 38 patients and their providers. Half of PainAPP users shared their summaries with their providers. Patients rated PainAPP highly in all areas. All users would recommend it to others with chronic pain; nearly all trusted the information and said it helped them think about my treatment goals (94%), understand my chronic pain (82%), make the most of my next doctor's visit (82%), and not want to use opioids (73%). Beta-testing revealed challenges delivering the tool and summary report to patients and providers in a timely manner and obtaining provider feedback.

Conclusions: PainAPP appears feasible for use, but further adaptation and testing is needed to assess its impact on patients and providers.

Trial registration: This study was approved by the University of New England Independent Review Board for the Protection of Human Subjects in Research (012616-019) and was registered with ClinicalTrials.gov (protocol ID: NCT03425266) prior to enrollment. The trial was prospectively registered and was approved on February 7, 2018.

Conflict of interest statement

NC has received consulting fees and research contracts from various entities through her contract research organization, Five Islands Consulting, LLC. Her paid and unpaid research and consulting have included developing and/or evaluating shared decision-making tools for multiple sclerosis (MS), aortic stenosis, atrial fibrillation, chronic pain, sleep apnea, and neurocritical care. Paid consulting included advising Miami University (assessing the quality of medical information), Biogen through their MS Quality Steering Committee, Emmi Solutions in developing decision aids, Wolters Kluwer in updating decision aids, Janssen Scientific Affairs, LLC (training, decision aids), 3D Communications (consulting), Epi-Q (consulting), Synchrony Group (consulting), Mallinckrodt’s SpecGx LLC (one-time consulting fee and reimbursement of travel), AceRx (one-time consulting fee and reimbursement of travel), Pacific Northwest University (travel expenses and honoraria), EMD-Serono (travel expenses, speaker fees, and research funding), and Edwards Lifesciences (research funding).

She serves as an unpaid mentor for 2 NIH training (K) grants developing decision tools in the areas of sleep apnea and neurocritical care. She received an independent research grant from MSAA (Multiple Sclerosis Association of America), Pfizer (in the area of chronic pain) and a research grant from Biogen (in the area of MS). She has 2 research grants under consideration: the Association of Community Cancer Centers (ACCC) and EMD Serono.

These authors declare that they have no competing interests: LN, VS, BT, NG, RE, EM, FS, SG, SH, NN, LP, MS.

Figures

Fig. 1
Fig. 1
Overview of cognitive mapping methods used for PainAPP
Fig. 2
Fig. 2
Content diagram of PainAPP
Fig. 3
Fig. 3
Sample summary of patient treatment goals and preference
Fig. 4
Fig. 4
Sample provider summary generated by PainAPP
Fig. 5
Fig. 5
Cognitive map of patient treatment goals (n = 13)
Fig. 6
Fig. 6
Overview of subject enrollment for beta-testing
Fig. 7
Fig. 7
Findings from Beta-testing of PainAPP versus Control (n = 24), prior to provider appointment

References

    1. Care and Education Committee on Advancing Pain Research, IOM . Relieving pain in America: a blueprint for transforming prevention, care, education, and research. Washington, DC: National Academies Press; 2011.
    1. Xu JQ, Murphy SL, Kochanek KD, Bastian B, Arias E. Deaths: final data for 2016. Natl Vital Stat Rep. 2018;67(5):1–76.
    1. National Vital Statistics System . Mortality multiple cause-of-death public use data file documentation. Hyattsville: US Department of Health and Human Services; National Center for Health Statistics; 2015.
    1. Hedegaard H, Minino AM, Warner M. Drug overdose deaths in the United States, 1999-2017. NCHS data Bried. 2018. pp. 1–8.
    1. Gomes T, Tadrous M, Mamdani MM, Paterson JM, Juurlink DN. The burden of opioid-related mortality in the United States. JAMA Netw Open. 2018;1(2):180217. doi: 10.1001/jamanetworkopen.2018.0217.
    1. Catan T, Perez Evan. A Pain-Drug Champion Has Second Thoughts. Wall Street Journal. 2012. .
    1. Mezei L, Murinson BB. Assessment of pain education in N American medical schools. J Pain. 2011;12(12):1199–1208. doi: 10.1016/j.jpain.2011.06.006.
    1. Jeffery MM, Butler M, Stark A, Kane RL. Multidisciplinary pain programs for chronic noncancer pain. Rockville: Agency for Healthcare Research and Quality; 2011.
    1. Frantsve LM, Kerns RD. Patient–provider interactions in the management of chronic pain: current findings within the context of shared medical decision making. Pain Med. 2007;8(1):25–35. doi: 10.1111/j.1526-4637.2007.00250.x.
    1. United States General Accounting Offi ce (GAO) Report to the Congressional Re-questers . Prescription drugs: OxyContin abuse and. Diversion and efforts to address the problem. 2003.
    1. Substance Abuse and Mental Health Services Administration, Center for Sub-stance Abuse Treatment . Methadone-associated mortality: report of a National Assessment. 2004.
    1. Matthias MS, Krebs EE, Collins LA, Bergman AA, Coffing J, Bair MJ. "I'm not abusing or anything": patient-physician communication about opioid treatment in chronic pain. Patient Educ Couns. 2013;93(2):197–202. doi: 10.1016/j.pec.2013.06.021.
    1. Marbach J, Lennon MC, Link BG, Dohrenwend BP. Losing face: sources of stigma as perceived by chronic facial pain patients. J Behav Med. 1990;13:583–604. doi: 10.1007/BF00844736.
    1. Edwards LC. The pain beliefs questionnaire: an investigation of beliefs in the causes and consequences of pain. Pain. 1992;51(3):267–272. doi: 10.1016/0304-3959(92)90209-T.
    1. Lillrank A. Back pain and the resolution of diagnostic uncertainty in illness narratives. Soc Sci Med. 2003;57:1045–1054. doi: 10.1016/S0277-9536(02)00479-3.
    1. Upshur CC, Bacigalupe G, Luckmann R. “They don’t want anything to do with you”: patient views of primary care management of chronic pain. Pain Med. 2010;11:1791–1798. doi: 10.1111/j.1526-4637.2010.00960.x.
    1. Dobscha SK, Corson K, Flores JA, Tansill EC, Gerrity MS. Veterans affairs primary care clinicians’ attitudes toward chronic pain and correlates of opioid prescribing rates. Pain Med. 2008;9:564–571. doi: 10.1111/j.1526-4637.2007.00330.x.
    1. Matthias MS, Parpart AL, Nyland KA, Huffman MA, Stubbs DL, Sargent C, et al. The patient–provider relationship in chronic pain care: providers’ perspectives. Pain Med. 2010;11:1688–1697. doi: 10.1111/j.1526-4637.2010.00980.x.
    1. Haythronthwaite JA, Fauerbach JA. Assessment of acute pain, pain relief and patient satisfaction. In: Melzack R, Turk DC, editors. Handbook of pain assessment. 2. New York: The Guilford Press; 2001. pp. 417–430.
    1. Jamison RN, Ross MJ, Hoopman P, et al. Assessment of postoperative pain management: patient satisfaction and perceived helpfulness. Clin J Pain. 1997;12:229–236. doi: 10.1097/00002508-199709000-00008.
    1. Comley AL, DeMeyer E. Assessing patient satisfaction with pain management through a continuous quality of improvement effort. J Pain Symptom Manag. 2001;21:27–40. doi: 10.1016/S0885-3924(00)00229-3.
    1. McCracken LM, Evon D, Darapas ET. Satisfaction with treatment for chronic pain in a specialty service: primary prospective results. Eur J Pain. 2002;6:387–393. doi: 10.1016/S1090-3801(02)00042-3.
    1. Nelson KA. Consumer decision making and image theory: understanding value-laden decisions. J Consum Psychol. 2004;14:28–40. doi: 10.1207/s15327663jcp1401&2_5.
    1. Reyna VF. A theory of medical decision making and health: fuzzy trace theory. Med Decis Mak. 2008;28:850–865. doi: 10.1177/0272989X08327066.
    1. Lockwood S. “Evidence of me” in evidence-based medicine? BMJ. 2004;329:1033–1035. doi: 10.1136/bmj.329.7473.1033.
    1. Karel MJ. The assessment of values in medical decision making. J Aging Stud. 2000;14:403–422. doi: 10.1016/S0890-4065(00)80005-5.
    1. Turner JA, Deyo RA, Loeser JD, Von Korff M, Fordyce WE. The importance of placebo effects in pain treatment and research. JAMA. 1994;271(20):1609–1614. doi: 10.1001/jama.1994.03510440069036.
    1. Henry SG, Bell RA, Fenton JJ, Kravitz RL. Goals of chronic pain management: do patients and primary care physicians agree and does it matter? Clin J Pain. 2017;33(11):955–961. doi: 10.1097/AJP.0000000000000488.
    1. Jecker NS. The role of intimate others in medical decision making. Gerontol. 1990;30:65–71. doi: 10.1093/geront/30.1.65.
    1. Hornberger JC, Habraken H, Bloch DA. Minimum data needed on patient preferences for accurate, efficient medical decision making. Med Care. 1995;33:297–310. doi: 10.1097/00005650-199503000-00008.
    1. Makoul G. Perpetuating passivity: reliance and reciprocal determinism in physician-patient interaction. J Health Commun. 1998;3:233–259. doi: 10.1080/108107398127355.
    1. Street RL, Jr, Gordon HS, Ward MM, Krupat E, Kravitz RL. Patient participation in medical consultations: why some patients are more involved than others. Med Care. 2005;43:960–969. doi: 10.1097/01.mlr.0000178172.40344.70.
    1. Edwards A, Elwyn G. Involving patients in decision making and communicating risk: a longitudinal evaluation of doctors' attitudes and confidence during a randomized trial. J Eval Clin Pract. 2004;10(3):431–437. doi: 10.1111/j.1365-2753.2004.00502.x.
    1. Col NF. Patient Health Communication to Improve Shared Decision Making. Chapter 17. In: Fischhoff B, Brewer NT, Downs JS, editors. U.S. Department of Health and Human Services, Food and Drug Administration, Risk Communication Advisory Committee Communicating risks and benefits: an evidence-based User's guide. . Accessed 10 Sept 2020.
    1. Street RL., Jr Information-giving in medical consultations: the influence of patients’ communicative styles and personal characteristics. Soc Sci Med. 1991;32(5):541–548. doi: 10.1016/0277-9536(91)90288-N.
    1. Gordon HS, Street RL, Jr, Sharf BF, Souchek J. Racial differences in doctors’ information-giving and patients’ participation. Cancer. 2006;107:1313–1320. doi: 10.1002/cncr.22122.
    1. Street RL, Jr, Richardson MN, Cox V, Suarez-Almazor ME. (Mis)understanding in patient-health care provider communication about total knee replacement. Arthritis Rheum. 2009;61:100–107. doi: 10.1002/art.24371.
    1. Frosch DL, May SG, Rendle KA, Tietbohl C, Elwyn G. Being labeled ‘difficult’ among key obstacles to shared decision making. Health Aff (Millwood) 2012;31(5):1030–1038. doi: 10.1377/hlthaff.2011.0576.
    1. Stacey D, Legare F, Lewis K, Barry MJ, Bennett CL, Eden KB. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2017;4:CD001431.
    1. Bowen E, Nayfe R, Milburn N, Mayo H, Reid MC, Fraenkel L, et al. Do decision aids benefit patients with chronic musculoskeletal pain? A systematic review. Pain Med. 2019. 10.1093/pm/pnz280 Epub ahead of print] PubMed PMID: 31880805.
    1. Bishop FL, Greville-Harris M, Bostock J, et al. Supporting informed choice in acupuncture: effects of a new person-, evidence- and theory-based website for patients with back pain. Acupunct Med. 2019;37(2):98–106. doi: 10.1177/0964528419827228.
    1. Fraenkel L, Rabidou N, Wittink D, Fried T. Improving informed decision-making for patients with knee pain. J Rheumatol. 2007;34(9):1894–1898.
    1. Patel S, Ngunjiri A, Hee SW, et al. Primum non no- cere: shared informed decision making in low back pain—a pilot cluster randomised trial. BMC Musculoskelet Disord. 2014;15(1):282. doi: 10.1186/1471-2474-15-282.
    1. Weymann N, Dirmaier J, von Wolff A, Kriston L, Harter M. Effectiveness of a web-based tailored inter- active health communication application for patients with type 2 diabetes or chronic low back pain: randomized controlled trial. J Med Internet Res. 2015;17(3):e53. doi: 10.2196/jmir.3904.
    1. Durand M-A, Stiel M, Boivin J, Elwyn G. Where is the theory? Evaluating the theoretical frameworks described in decision support technologies. Patient Educ Couns. 2008;71(1):125–135. doi: 10.1016/j.pec.2007.12.004.
    1. Delbecq AL, Van de Ven AH, Gustafson DH. Group techniques for program planning: a guide to nominal group and Delphi processes. Middleton: Green Briar Press; 1975.
    1. Potter M, Gordon S, Hamer P. The nominal group technique: a useful consensus methodology in physiotherapy research. N Z J Physiother. 2004;32(3):126–130.
    1. Gallagher M, Hares T, Spencer J, Bradshaw C, Webb I. The nominal group technique: a research tool for general practice? Fam Pract. 1993;10(1):76–81. doi: 10.1093/fampra/10.1.76.
    1. Scott D, Deadneck D. The nominal group technique applications for training needs. Train Dev. 1982;36(6):26–33.
    1. Witteman HO, Dansokho SC, Colquhoun H, Coulter A, Dugas M, Fagerlin A, et al. User-centered design and the development of patient decision aids: protocol for a systematic review. Syst Rev. 2015;4(1):11. doi: 10.1186/2046-4053-4-11.
    1. Coulter A, Stilwell D, Kryworuchko J, Mullen PD, Ng CJ, van der Weijden T. A systematic development process for patient decision aids. BMC Med Inform Decis Mak. 2013;13 Suppl 2(Suppl 2):S2. doi: 10.1186/1472-6947-13-S2-S2.
    1. Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011;10:CD001431.
    1. Col NF, Solomon AJ, Springmann V, Ionete C, Alvarez E, Tierman B, et al. Evaluation of a novel preference assessment tool for patients with multiple sclerosis. Int J MS Care. 2018;20(6):260–267. doi: 10.7224/1537-2073.2017-021.
    1. Col NF, Solomon AJ, Springmann V, Garbin C, Ionete C, Pbert L, et al. Whose preferences matter? A patient-centered approach for eliciting treatment goals. Med Decis Mak. 2018;38(1):44–55. doi: 10.1177/0272989X17724434.
    1. Schiffman SS, Reynolds ML, Young FW. Introduction to multidimensional scaling: theory, methods, and applications. New York: Academic; 1981.
    1. Fitzgerald LF, Hubert LJ. Multidimensional scaling: some possibilities for counseling psychology. J Couns Psychol. 1987;34(4):469–480. doi: 10.1037/0022-0167.34.4.469.
    1. Kruskal JB, Wish M. Multidimensional scaling. Beverly Hills: Sage Publications; 1978.
    1. Aldenderfer MS, Blashfield RK. Cluster analysis. Beverly Hills: Sage Publications; 1984.
    1. de Vries P, Stiggelbout K, Feldman-Stewart D. Theory-informed design of VCM. Soc Sci Med. 2013;77:156–163. doi: 10.1016/j.socscimed.2012.11.020.
    1. Potter RB, Beach LR. Imperfect information in pre-choice screening of options. Org Beh Human Dec Proc. 1994;59(2):313–329. doi: 10.1006/obhd.1994.1062.
    1. Elwyn G, O’Connor A, Stacey D, et al. Developing a quality criteria framework for patient decision aids: online international Delphi consensus process. BMJ. 2006;333(7565):417. doi: 10.1136/.
    1. Available at . Accessed 9/11/2020.
    1. Washington TA, Fanciullo GJ, Sorensen JA, Baird JC. Quality of chronic pain websites. Pain Med. 2008;9(8):994–1000. doi: 10.1111/j.1526-4637.2008.00419.x.
    1. CAHPSR Clinician & Group Survey . Overview of the Questionnaire. 2015.
    1. Edwards A, Elwyn G, Hood K, et al. The development of COMRADE—a patient-based outcome measure to evaluate the effectiveness of risk communication and treatment decision-making in consultations. Patient Educ Couns. 2003;50:311–322. doi: 10.1016/S0738-3991(03)00055-7.
    1. Banta-Green CJ, Von Korff M, Sullivan MD, Merrill JO, Doyle SR, Saunders K. The prescribed opioids difficulties scale: a patient-centered assessment of problems and concerns. Clin J Pain. 2010;26(6):489–497. doi: 10.1097/AJP.0b013e3181e103d9.
    1. Hamann J, Langer B, Winkler V, Busch R, Cohen R, Leucht S, et al. Shared decision making for in-patients with schizophrenia. Acta Psychiatr Scand. 2006;114(4):265–273. doi: 10.1111/j.1600-0447.2006.00798.x.
    1. Crutzen R, Cyr D, de Vries NK. The role of user control in adherence to and knowledge gained from a website: randomized comparison between a tunneled version and a freedom-of-choice version. J Med Internet Res. 2012;14(2):e45. doi: 10.2196/jmir.1922.
    1. Yank V, Laurent D, Plant K, Lorig K. Web-based self-management support training for health professionals: a pilot study. Patient Educ Couns. 2013;9(1):29–37. doi: 10.1016/j.pec.2012.09.003.
    1. Skeff KM, Stratos GA. Georgetite, Bergen MR. evaluation of a medical faculty development program: a comparison of traditional pre/post and retrospective pre/post self-assessment ratings. Eval Health Prof. 1992;15(3):50–366. doi: 10.1177/016327879201500307.
    1. Howard GS, Ralph KM, Gulanick NA, Maxwell SE, Nance DW, Gerber SK. Internal invalidity in pretest-posttest self-report evaluations and a re-evaluation of retrospective pretests. Appl Psychol Meas. 1979;3(1):1–23. doi: 10.1177/014662167900300101.
    1. Howard GS, Millhan J, Slaten S, O’Donnell L. Influence of subject response-style effects on retrospective measures. Appl Psychol Meas. 1981;5:144–150. doi: 10.1177/014662168100500113.
    1. Pratt C, McGuigan M, Katzev R. Measuring program outcomes: using retrospective pretest methodology. Am J Eval. 2000;21:341–349. doi: 10.1177/109821400002100305.
    1. Nimon K, Zigarmi D, Allen JM. Measures of program effectiveness based on retrospective pretest data: Are all created equal? Am J Eval. 2010;32:8–28.
    1. Baker FB, Hubert LJ. Measuring the power of hierarchical cluster analysis. J Am Stat Assoc. 1975;70(349):31–38. doi: 10.1080/01621459.1975.10480256.
    1. Rodgers JL. Matrix and stimulus sample sizes in the weighted MDS model: empirical metric recovery functions. Appl Psych Meas. 1991;15:71–77. doi: 10.1177/014662169101500107.
    1. Dahlhamer J, Lucas J, Zelaya C, et al. Prevalence of chronic pain and high-impact chronic pain among adults — United States, 2016. MMWR Morb Mortal Wkly Rep. 2018;67:1001–1006. doi: 10.15585/mmwr.mm6736a2.
    1. A to Z Inventory of Decision Aids. The Ottawa Hospital Research Institute. , Accessed on 11 Sep 2020.
    1. Zhao P, Yoo I, Lancey R, et al. Mobile applications for pain management: an app analysis for clinical usage. BMC Med Inform Decis Mak. 2019;19:106. doi: 10.1186/s12911-019-0827-7.
    1. Barry M. Stop the silent misdiagnosis: patients’ preferences matter. BMJ. 2012;345:e6572. doi: 10.1136/bmj.e6572.
    1. Kistin C, Silverstein M. Pilot studies. A critical but potentially misused component of interventional research. JAMA. 2015;314(15):1561–1562. doi: 10.1001/jama.2015.10962.

Source: PubMed

3
Subscribe