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BMC Medical Informatics and Decision Making

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Open Access 01-12-2023 | Research

Leveraging web-based prediction calculators to set patient expectations for elective spine surgery: a qualitative study to inform implementation

Authors: Trevor A. Lentz, Byron F. Stephens, Amir M. Abtahi, Jacob Schwarz, Andrew J. Schoenfeld, Bethany A. Rhoten, Shannon Block, Alex O’Brien, Kristin R. Archer

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

Abstract

Background

Prediction calculators can help set outcomes expectations following orthopaedic surgery, however effective implementation strategies for these tools are unknown. This study evaluated provider and patient perspectives on clinical implementation of web-based prediction calculators developed using national prospective spine surgery registry data from the Quality Outcomes Database.

Methods

We conducted semi-structured interviews in two health systems, Vanderbilt University Medical Center (VUMC) and Duke University Health System (DUHS) of orthopedic and neurosurgery health care providers (VUMC: n = 19; DUHS: n = 6), health care administrators (VUMC: n = 9; DUHS: n = 9), and patients undergoing elective spine surgery (VUMC: n = 16). Qualitative template analysis was used to analyze interview data, with a focus on end-user perspectives regarding clinical implementation of web-based prediction tools.

Results

Health care providers, administrators and patients overwhelmingly supported the use of the calculators to help set realistic expectations for surgical outcomes. Some clinicians had questions about the validity and applicability of the calculators in their patient population. A consensus was that the calculators needed seamless integration into clinical workflows, but there was little agreement on best methods for selecting which patients to complete the calculators, timing, and mode of completion. Many interviewees expressed concerns that calculator results could influence payers, or expose risk of liability. Few patients expressed concerns over additional survey burden if they understood that the information would directly inform their care.

Conclusions

Interviewees had a largely positive opinion of the calculators, believing they could aid in discussions about expectations for pain and functional recovery after spine surgery. No single implementation strategy is likely to be successful, and strategies vary, even within the same healthcare system. Patients should be well-informed of how responses will be used to deliver better care, and concerns over how the calculators could impact payment and liability should be addressed prior to use. Future research is necessary to determine whether use of calculators improves management and outcomes for people seeking a surgical consult for spine pain.

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Dieleman JL, Cao J, Chapin A, Chen C, Li Z, Liu A, et al. US Health Care spending by Payer and Health Condition, 1996–2016. JAMA. 2020;323:863–84.CrossRefPubMedPubMedCentral

Institute of Medicine (US). Committee on Advancing Pain Research, Care, and Education. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research [Internet]. Washington (DC): National Academies Press (US); 2011 [cited 2020 Apr 28]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK91497/.

Deyo RA, Mirza SK, Martin BI, Kreuter W, Goodman DC, Jarvik JG, Trends. Major Medical Complications, and charges Associated with surgery for lumbar spinal stenosis in older adults. JAMA. 2010;303:1259–65.CrossRefPubMedPubMedCentral

Ragab A, Deshazo RD. Management of back pain in patients with previous back surgery. Am J Med. 2008;121:272–8.CrossRefPubMed

Weiner DK, Kim Y-S, Bonino P, Wang T. Low back pain in older adults: are we utilizing healthcare resources wisely? Pain Med. 2006;7:143–50.CrossRefPubMed

Centers for Medicare & Medicaid Services (CMS) H. Medicare program; hospital inpatient value-based purchasing program. Final rule. 2011.

Sciubba DM, Pennington Z, Ehresman J. Guest Editorial: Predictive Analytics, calculators and cost modeling in spine surgery. Global spine Journal. Volume 11. SAGE Publications Inc; 2021. pp. 4S–6S.

Damberg CL, Sorbero ME, Lovejoy SL, Martsolf GR, Raaen L, Mandel D. Measuring success in Health Care Value-Based Purchasing Programs: findings from an Environmental scan, Literature Review, and Expert Panel Discussions. Rand Health Q. 2014;4:9.PubMedPubMedCentral

Sivaganesan A, Khan I, Pennings JS, Roth SG, Nolan ER, Oleisky ER, et al. Why are patients dissatisfied after spine surgery when improvements in disability and pain are clinically meaningful? Spine J. 2020;20:1535–43.CrossRefPubMed

10.

Asher AM, Oleisky ER, Pennings JS, Khan I, Sivaganesan A, Devin CJ, et al. Measuring clinically relevant improvement after lumbar spine surgery: is it time for something new? Spine J. 2020;20:847–56.CrossRefPubMed

11.

Khan I, Pennings JS, Devin CJ, Asher AM, Oleisky ER, Bydon M, et al. Clinically meaningful improvement following cervical spine surgery: 30% reduction Versus Absolute Point-change MCID values. Spine (Phila Pa 1976). 2021;46:717–25.CrossRefPubMed

12.

Hamilton DF, Lane JV, Gaston P, Patton JT, MacDonald D, Simpson AHRW, et al. What determines patient satisfaction with surgery? A prospective cohort study of 4709 patients following total joint replacement. BMJ Open British Medical Journal Publishing Group. 2013;3:e002525.

13.

Mannion AF, Junge A, Elfering A, Dvorak J, Porchet F, Grob D. Great expectations: really the novel predictor of Outcome after spinal surgery? Spine. 2009;34:1590–9.CrossRefPubMed

14.

Asher AL, Devin CJ, Kerezoudis P, Nian H, Alvi MA, Khan I, et al. Predictors of patient satisfaction following 1- or 2-level anterior cervical discectomy and fusion: insights from the Quality Outcomes Database. J Neurosurgery: Spine Am Association Neurol Surg. 2019;31:835–43.

15.

Cha EDK, Lynch CP, Jadczak CN, Mohan S, Geoghegan CE, Singh K. Meeting patient expectations or achieving a Minimum clinically important difference: predictors of satisfaction among lumbar Fusion Patients. Asian Spine J. 2022;16:478–85.CrossRefPubMed

16.

Rampersaud YR, Canizares M, Perruccio AV, Abraham E, Bailey CS, Christie SD, et al. Fulfillment of patient expectations after spine surgery is critical to patient satisfaction: a cohort study of spine surgery patients. Neurosurgery. 2022;91:173–81.CrossRefPubMedPubMedCentral

17.

Eastwood D, Manson N, Bigney E, Darling M, Richardson E, Paixao R, et al. Improving postoperative patient reported benefits and satisfaction following spinal fusion with a single preoperative education session. Spine J. 2019;19:840–5.CrossRefPubMed

18.

Lubelski D, Hersh A, Azad TD, Ehresman J, Pennington Z, Lehner K, et al. Prediction models in degenerative spine surgery: a systematic review. Global Spine J. 2021;11:79S–88S.CrossRefPubMedPubMedCentral

19.

Caplan IF, Winter E, Glauser G, Goodrich S, McClintock SD, Hume EL, et al. Composite score for prediction of 30-day orthopedic surgery outcomes. J Orthop Res. 2020;38:2189–96.CrossRefPubMed

20.

Dimentberg R, Caplan IF, Winter E, Glauser G, Goodrich S, McClintock SD, et al. Prediction of adverse outcomes within 90 days of surgery in a heterogeneous orthopedic surgery Population. J Healthc Qual. 2021;43:e53–63.CrossRefPubMed

21.

Anis HK, Strnad GJ, Klika AK, Zajichek A, Spindler KP, Barsoum WK, et al. Developing a personalized outcome prediction tool for knee arthroplasty. The Bone & Joint Journal. Volume 102–B. The British Editorial Society of Bone & Joint Surgery; 2020. pp. 1183–93.

22.

Archer KR, Bydon M, Khan I, Nian H, Pennings JS, Harrell FE, et al. Development and validation of cervical prediction models for patient-reported outcomes at 1 year after cervical spine surgery for Radiculopathy and Myelopathy. Spine (Phila Pa 1976). 2020;45:1541–52.CrossRefPubMed

23.

McGirt MJ, Bydon M, Archer KR, Devin CJ, Chotai S, Parker SL, et al. An analysis from the Quality Outcomes Database, Part 1. Disability, quality of life, and pain outcomes following lumbar spine surgery: predicting likely individual patient outcomes for shared decision-making. J Neurosurg Spine. 2017;27:357–69.CrossRefPubMed

24.

Curtin P, Conway A, Martin L, Lin E, Jayakumar P, Swart E. Compilation and analysis of web-based Orthopedic Personalized Predictive Tools: a scoping review. J Pers Med. 2020;10:223.CrossRefPubMedPubMedCentral

25.

Tetreault LA, Côté P, Kopjar B, Arnold P, Fehlings MG, AOSpine North America and International Clinical Trial Research Network. A clinical prediction model to assess surgical outcome in patients with cervical spondylotic myelopathy: internal and external validations using the prospective multicenter AOSpine North American and international datasets of 743 patients. Spine J. 2015;15:388–97.CrossRefPubMed

26.

White HJ, Bradley J, Hadgis N, Wittke E, Piland B, Tuttle B, et al. Predicting patient-centered outcomes from spine surgery using Risk Assessment Tools: a systematic review. Curr Rev Musculoskelet Med. 2020;13:247–63.CrossRefPubMedPubMedCentral

27.

Devin CJ, Bydon M, Alvi MA, Kerezoudis P, Khan I, Sivaganesan A, et al. A predictive model and nomogram for predicting return to work at 3 months after cervical spine surgery: an analysis from the Quality Outcomes Database. Neurosurg Focus. 2018;45:E9.CrossRefPubMed

28.

Asher AL, Devin CJ, Archer KR, Chotai S, Parker SL, Bydon M, et al. An analysis from the Quality Outcomes Database, Part 2. Predictive model for return to work after elective surgery for lumbar degenerative disease. J Neurosurg Spine. 2017;27:370–81.CrossRefPubMed

29.

Sittig DF, Singh H. A New Socio-technical model for studying Health Information Technology in Complex Adaptive Healthcare Systems. Qual Saf Health Care. 2010;19:i68–74.CrossRefPubMed

30.

Turk DC, Dworkin RH, Revicki D, Harding G, Burke LB, Cella D, et al. Identifying important outcome domains for chronic pain clinical trials: an IMMPACT survey of people with pain. Pain. 2008;137:276–85.CrossRefPubMed

31.

Ostelo RWJG, de Vet HCW. Clinically important outcomes in low back pain. Best Pract Res Clin Rheumatol. 2005;19:593–607.CrossRefPubMed

32.

Whitebird RR, Solberg LI, Norton CK, Ziegenfuss JY, Asche SE, Grossman ES. What outcomes matter to patients after joint or spine surgery? J Patient Cent Res Rev. 2020;7:157–64.CrossRefPubMedPubMedCentral

33.

Archer KR, Nian H, Khan I, Pennings JS. Streamlining the QOD Web-based Calculator for Clinical Integration: Development and Validation of a Reduced Prediction Model for Lumbar Spine Surgery. Spine. 2022;In Press.

34.

Brooks J, McCluskey S, Turley E, King N. The utility of Template Analysis in qualitative psychology research. Qualitative Res Psychol Routledge. 2015;12:202–22.CrossRef

35.

Porter A, Kingston MR, Evans BA, Hutchings H, Whitman S, Snooks H. It could be a ‘Golden Goose’: a qualitative study of views in primary care on an emergency admission risk prediction tool prior to implementation. BMC Fam Pract. 2016;17:1.CrossRefPubMedPubMedCentral

36.

Evans BA, Dale J, Davies J, Hutchings H, Kingston M, Porter A, et al. Implementing emergency admission risk prediction in general practice: a qualitative study. Br J Gen Pract. 2022;72:e138–47.CrossRefPubMed

37.

Trivedi MH, Daly EJ, Kern JK, Grannemann BD, Sunderajan P, Claassen CA. Barriers to implementation of a computerized decision support system for depression: an observational report on lessons learned in “real world” clinical settings. BMC Med Inf Decis Mak. 2009;9:6.CrossRef

38.

Klarenbeek SE, Schuurbiers-Siebers OCJ, van den Heuvel MM, Prokop M, Tummers M. Barriers and facilitators for implementation of a computerized clinical decision support system in Lung Cancer Multidisciplinary Team Meetings-A qualitative Assessment. Biology (Basel). 2020;10:9.PubMed

39.

Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366:447–53.CrossRefPubMed

40.

Benjamin R. Assessing risk, automating racism. Science. 2019;366:421–2.CrossRefPubMed

41.

Bærøe K, Gundersen T, Henden E, Rommetveit K. Can medical algorithms be fair? Three ethical quandaries and one dilemma. BMJ Health Care Inform. 2022;29:e100445.CrossRefPubMedPubMedCentral

Title: Leveraging web-based prediction calculators to set patient expectations for elective spine surgery: a qualitative study to inform implementation
Authors: Trevor A. Lentz
Byron F. Stephens
Amir M. Abtahi
Jacob Schwarz
Andrew J. Schoenfeld
Bethany A. Rhoten
Shannon Block
Alex O’Brien
Kristin R. Archer
Publication date: 01-12-2023
Publisher: BioMed Central
Published in: BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-023-02234-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Leveraging web-based prediction calculators to set patient expectations for elective spine surgery: a qualitative study to inform implementation

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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