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Exploration of an Automated Approach for Receiving Patient Feedback After Outpatient Acute Care Visits

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ABSTRACT

BACKGROUND

To improve and learn from patient outcomes, particularly under new care models such as Accountable Care Organizations and Patient-Centered Medical Homes, requires establishing systems for follow-up and feedback.

OBJECTIVE

To provide post-visit feedback to physicians on patient outcomes following acute care visits.

DESIGN

A three-phase cross-sectional study [live follow-up call three weeks after acute care visits (baseline), one week post-visit live call, and one week post-visit interactive voice response system (IVRS) call] with three patient cohorts was conducted. A family medicine clinic and an HIV clinic participated in all three phases, and a cerebral palsy clinic participated in the first two phases. Patients answered questions about symptom improvement, medication problems, and interactions with the healthcare system.

PATIENTS

A total of 616 patients were included: 142 from Phase 1, 352 from Phase 2 and 122 from Phase 3.

MAIN MEASURES

Primary outcomes included: problem resolution, provider satisfaction with the system, and comparison of IVRS with live calls made by research staff.

KEY RESULTS

During both live follow-up phases, at least 96 % of patients who were reached completed the call compared to only 48 % for the IVRS phase. At baseline, 98 of 113 (88 %) patients reported improvement, as well as 167 of 196 (85 %) in the live one-week follow-up. In the one-week IVRS phase, 25 of 39 (64 %) reported improvement. In all phases, the majority of patients in both the improved and unimproved groups had not contacted their provider or another provider. While 63 % of providers stated they wanted to receive patient feedback, they varied in the extent to which they used the feedback reports.

CONCLUSIONS

Many patients who do not improve as expected do not take action to further address unresolved problems. Systematic follow-up/feedback mechanisms can potentially identify and connect such patients to needed care.

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REFERENCES

  1. Berwick DM. Launching accountable care organizations—the proposed rule for the Medicare Shared Savings Program. New Engl J Med. 2011;364(16):e32. PubMed PMID: 21452999.

    Article  PubMed  Google Scholar 

  2. American College of Physicians. The advanced medical home: a patient-centered, physician-guided model of health care. Philadelphia: American College of Physicians; 2005.

  3. Davis K, Schoenbaum SC, Audet AM. A 2020 vision of patient-centered primary care. J Gen Intern Med. 2005;20(10):953–957. PubMed PMID: 16191145. Pubmed Central PMCID: 1490238.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Agency for Healthcare Research and Quality. Patient centered medical home resource center [January 6, 2013]. Available from: http://pcmh.ahrq.gov/.

  5. Engineering a Learning Healthcare System. A look at the future: workshop summary. Washington, DC: The National Academies Press; 2011.

  6. Schiff GD. Minimizing diagnostic error: the importance of follow-up and feedback. Am J Med. 2008;121(5 Suppl):S38–S42. PubMed PMID: 18440354.

    Article  PubMed  Google Scholar 

  7. Schiff GD, Bates DW. Can electronic clinical documentation help prevent diagnostic errors? New Engl J Med. 2010;362(12):1066–1069. PubMed PMID: 20335582.

    Article  CAS  PubMed  Google Scholar 

  8. Singh H, Graber M. Reducing diagnostic error through medical home-based primary care reform. JAMA. 2010;304(4):463–464. PubMed PMID: 20664048. Pubmed Central PMCID: 3120138.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Crandall B, Wears RL. Expanding perspectives on misdiagnosis. Am J Med. 2008;121(5 Suppl):S30–S33. PubMed PMID: 18440352.

    Article  PubMed  Google Scholar 

  10. Wears RL, Schiff GD. One cheer for feedback. Ann Emerg Med. 2005;45(1):24. PubMed PMID: 15635302.

    Article  PubMed  Google Scholar 

  11. Chern CH, How CK, Wang LM, Lee CH, Graff L. Decreasing clinically significant adverse events using feedback to emergency physicians of telephone follow-up outcomes. Ann Emerg Med. 2005;45(1):15–23. PubMed PMID: 15635301.

    Article  PubMed  Google Scholar 

  12. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161–167. PubMed PMID: 12558354.

    Article  PubMed  Google Scholar 

  13. Committee on Identifying and Preventing Medication Errors. Preventing medication errors: quality chasm series. Aspden P, Wolcott J, Bootman JL, Cronenwett LR, editors. Washington, DC: The National Academies Press; 2007.

  14. DeSalvo KB, Block JP, Muntner P, Merrill W. Predictors of variation in office visit interval assignment. Int J Qual Health Care. 2003;15(5):399–405. PubMed PMID: 14527983. Epub 2003/10/07. eng.

    Article  PubMed  Google Scholar 

  15. Haas JS, Amato M, Marinacci L, Orav EJ, Schiff GD, Bates DW. Do package inserts reflect symptoms experienced in practice?: assessment using an automated phone pharmacovigilance system with varenicline and zolpidem in a primary care setting. Drug Safety. 2012;35(8):623–628. PubMed PMID: 22764754. Epub 2012/07/07. eng.

    Article  CAS  PubMed  Google Scholar 

  16. Haas JS, Iyer A, Orav EJ, Schiff GD, Bates DW. Participation in an ambulatory e-pharmacovigilance system. Pharmacoepidemiology and Drug Safety. 2010;19(9):961–969. PubMed PMID: 20623512.

    Article  PubMed  Google Scholar 

  17. Haas JS, Klinger E, Marinacci LX, Brawarsky P, Orav EJ, Schiff GD, et al. Active pharmacovigilance and healthcare utilization. American Journal of Managed Care. 2012;18(11):e423–e428. PubMed PMID: 23198749. Epub 2012/12/04. eng.

    PubMed  Google Scholar 

  18. Byrom B. Using IVRS in Clinical Trial Management. Appl Clin Trials. 2002;2002:36–42.

    Google Scholar 

  19. Willig JH, Krawitz M, Panjamapirom A, Ray MN, Nevin CR, English TM, et al. Closing the feedback loop: an interactive voice response system to provide follow-up and feedback in primary care settings. J Med Syst. 2013;37(2):9905. PubMed PMID: 23340825.

    Google Scholar 

  20. Houser SH, Ray MN, Maisiak R, Panjamapirom A, Willig J, Schiff GD, et al. Telephone follow-up in primary care: can interactive voice response calls work? Stud Health Technol Inform. 2013;192:112–116. PubMed PMID: 23920526.

  21. Allscripts [04/25/2013]. Available from: http://www.allscripts.com/.

  22. WorldVista [04/25/2013]. Available from: http://worldvista.org/.

  23. SPSS Statistics for Windows. 17.0 ed. Chicago, IL: SPSS, Inc.; 2008.

  24. Maisiak RS, Ray MN, Panjamapirom A, Houser S, Willig JH, English TM, et al. The general medication adherence (GMA) scale: Psychometric analysis of a new scale for the primary care setting. Washington, DC: Society for Behavioral Medicine; 2011.

    Google Scholar 

  25. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. New Engl J Med. 2010;363(6):501–504. PubMed PMID: 20647183.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

Contributors

No other contributors.

Funders

This research was supported by grant #R18HS017060 from the Agency for Healthcare Research and Quality (AHRQ), and was also supported by grant # P30 AI027767 from NIH-NIAID.

Prior presentations

Portions of this manuscript were presented at the following conferences or lectures listed below.

Berner E et al. (March 2013) Automated Follow-up of Patients in Ambulatory Care: Physician and Patient Views. Presented at the 8th Annual AUPHA Academic Forum, HIMSS-2013, New Orleans, LA.

Berner ES, Burkhardt J, Houser S, et al. Closing the feedback loop to improve diagnostic quality. Presentation at AHRQ HIT Grantees Meeting; June 2010; Bethesda, MD.

Ray MN, Willig J, Cohen M, et al. Follow-up phone calls to improve patient safety in primary care: Issues encountered and lessons learned. Poster presentation at AHRQ HIT Grantees Meeting; June 2010; Bethesda, MD.

Berner ES, Ray MN, Schiff GD, et al. Closing the Feedback Loop to Improve Diagnostic Quality. Poster and abstract at AHRQ Annual HIT Annual Conference; September 7–10, 2008; Bethesda, MD.

Ray MN, Berner ES, Schiff GD, et al. Closing the Feedback Loop to Improve Diagnostic Quality. Poster presentation at Diagnostic Errors in Medicine Conference; June 2008; Phoenix, AZ.

Conflict of Interest

CDNA is copyrighted by the University of Alabama at Birmingham. Eta Berner, Midge Ray, James Willing, Marc Krawitz, and Anantachai Panjamapirom are CDNA inventors. Dr. Berner receives book royalties from Springer-Verlag London Ltd and Health Administration Press. Dr. Panjamapirom is employed by the Advisory Board Company. Dr. Willig has consulted with Qwest Diagnostics and received grants from Definicare. Mr. Krawitz is employed with CareFusion, is a co-owner of Physician Innovations, LLC, and is employed part time by the University of Phoenix.

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Correspondence to Eta S. Berner EdD.

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Berner, E.S., Ray, M.N., Panjamapirom, A. et al. Exploration of an Automated Approach for Receiving Patient Feedback After Outpatient Acute Care Visits. J GEN INTERN MED 29, 1105–1112 (2014). https://doi.org/10.1007/s11606-014-2783-3

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  • DOI: https://doi.org/10.1007/s11606-014-2783-3

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