PDF herunterladen
Appl Clin Inform 2020; 11(03): 497-514
DOI: 10.1055/s-0040-1714692
Research Article
Georg Thieme Verlag KG Stuttgart · New York

An Electronic Medical Record-Derived Individualized Performance Metric to Measure Risk-Adjusted Adherence with Perioperative Prophylactic Bundles for Health Care Disparity Research and Implementation Science

Michael H. Andreae 
1   Department of Anesthesiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States
,
Stephan R. Maman 
1   Department of Anesthesiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States
2   Penn State College of Medicine, Hershey, Pennsylvania, United States
,
Abrahm J. Behnam
1   Department of Anesthesiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania, United States
› Institutsangaben Funding Research reported in this publication was supported by the National Center for Advancing Translational Sciences (NIH grants TL1 TR002016 and UL1 TR002014). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The research reported was furthermore supported through the Donald E. Martin Professorship for Anesthesia and Pain Medicine, a career development endowment of the Department of Anesthesiology at Penn State Milton S. Hershey Medical Center.
Weitere Informationen

Publikationsverlauf

24. März 2020

01. Juni 2020

Publikationsdatum:
29. Juli 2020 (online)

   Lizenzen und Reprints

Abstract

Background Health care disparity persists despite vigorous countermeasures. Clinician performance is paramount for equitable care processes and outcomes. However, precise and valid individual performance measures remain elusive.

Objectives We sought to develop a generalizable, rigorous, risk-adjusted metric for individual clinician performance (MIP) derived directly from the electronic medical record (EMR) to provide visual, personalized feedback.

Methods We conceptualized MIP as risk responsiveness, i.e., administering an increasing number of interventions contingent on patient risk. We embedded MIP in a hierarchical statistical model, reflecting contemporary nested health care delivery. We tested MIP by investigating the adherence with prophylactic bundles to reduce the risk of postoperative nausea and vomiting (PONV), retrieving PONV risk factors and prophylactic antiemetic interventions from the EMR. We explored the impact of social determinants of health on MIP.

Results We extracted data from the EMR on 25,980 elective anesthesia cases performed at Penn State Milton S. Hershey Medical Center between June 3, 2018 and March 31, 2019. Limiting the data by anesthesia Current Procedural Terminology code and to complete cases with PONV risk and antiemetic interventions, we evaluated the performance of 83 anesthesia clinicians on 2,211 anesthesia cases. Our metric demonstrated considerable variance between clinicians in the adherence to risk-adjusted utilization of antiemetic interventions. Risk seemed to drive utilization only in few clinicians. We demonstrated the impact of social determinants of health on MIP, illustrating its utility for health science and disparity research.

Conclusion The strength of our novel measure of individual clinician performance is its generalizability, as well as its intuitive graphical representation of risk-adjusted individual performance. However, accuracy, precision and validity, stability over time, sensitivity to system perturbations, and acceptance among clinicians remain to be evaluated.

Keywords

health care disparity - perioperative care - postoperative nausea and vomiting - implementation science - electronic medical record

Protection of Human and Animal Subjects

The Penn State Health Institutional Review Board determined that the described work met the criteria for exempt research according to the policies of this institution and the provisions of applicable federal regulations (STUDY00007035).


Supplementary Material

 

  • References

  • 1 Link BG, Phelan J. Understanding racial and ethnic disparities in postsurgical complications occurring in U.S. hospitals. J Health Soc Behav 1995; (Spec No): 80-94
    PubMedGoogle Scholar
  • 2 Witt WP, Coffey RM, Lopez-Gonzalez L. , et al. Understanding racial and ethnic disparities in postsurgical complications occurring in U.S. hospitals. Health Serv Res 2017; 52 (01) 220-243
    CrossrefPubMedGoogle Scholar
  • 3 Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health 1997; 18: 341-378
    CrossrefPubMedGoogle Scholar
  • 4 Andreae MH, Andreae DA, Maman S. The ethics of universal health insurance. Anesth Analg 2020; 130 (03) e85-e87
    CrossrefPubMedGoogle Scholar
  • 5 Andreae MH, Gabry JS, Goodrich B, White RS, Hall C. Antiemetic prophylaxis as a marker of health care disparities in the national anesthesia clinical outcomes registry. Anesth Analg 2018; 126 (02) 588-599
    CrossrefPubMedGoogle Scholar
  • 6 Thorlby R, Jorgensen S, Siegel B, Ayanian JZ. How health care organizations are using data on patients' race and ethnicity to improve quality of care. Milbank Q 2011; 89 (02) 226-255
    CrossrefPubMedGoogle Scholar
  • 7 Andreae MH, White RS, Chen KY, Nair S, Hall C, Shaparin N. The effect of initiatives to overcome language barriers and improve attendance: a cross-sectional analysis of adherence in an inner city chronic pain clinic. Pain Med 2017; 18 (02) 265-274
    CrossrefPubMedGoogle Scholar
  • 8 Sequist TD, Fitzmaurice GM, Marshall R, Shaykevich S, Safran DG, Ayanian JZ. Physician performance and racial disparities in diabetes mellitus care. Arch Intern Med 2008; 168 (11) 1145-1151
    CrossrefPubMedGoogle Scholar
  • 9 Silber JH, Kennedy SK, Even-Shoshan O. , et al. Anesthesiologist direction and patient outcomes. Anesthesiology 2000; 93 (01) 152-163
    CrossrefPubMedGoogle Scholar
  • 10 Gaba DM. Anaesthesiology as a model for patient safety in health care. BMJ 2000; 320 (7237): 785-788
    CrossrefPubMedGoogle Scholar
  • 11 Atchabahian A, Andreae M. Long-term functional outcomes after regional anesthesia: a summary of the published evidence and a recent cochrane review. Refresh Courses Anesthesiol 2015; 43 (01) 15-26
    PubMedGoogle Scholar
  • 12 Maman S, Andreae M. 3025 individual anesthesia provider performance assessment. J Clin Transl Sci 2019; 3 (S1): 147 . Doi: 10.1017/cts.2019.334
    PubMedGoogle Scholar
  • 13 Skochelak SE, Hammoud MM, Lomis KD, Borkan JM, Gonzalo JD, Lawson LE, Starr SR. , eds. Health Systems Science, 2nd ed. Philadelphia, PA: Elsevier; 2020
    Google Scholar
  • 14 Kawachi I, Daniels N, Robinson DE. Health disparities by race and class: why both matter. Health Aff (Millwood) 2005; 24 (02) 343-352
    CrossrefPubMedGoogle Scholar
  • 15 Epstein RH, Dexter F, Patel N. Influencing anesthesia provider behavior using anesthesia information management system data for near real-time alerts and post hoc reports. Anesth Analg 2015; 121 (03) 678-692
    CrossrefPubMedGoogle Scholar
  • 16 Chau A, Ehrenfeld JM. Using real-time clinical decision support to improve performance on perioperative quality and process measures. Anesthesiol Clin 2011; 29 (01) 57-69
    CrossrefPubMedGoogle Scholar
  • 17 Hyder JA, Hebl JR. Performance measurement to demonstrate value. Anesthesiol Clin 2015; 33 (04) 679-696
    CrossrefPubMedGoogle Scholar
  • 18 Hyder JA, Niconchuk J, Glance LG. , et al. What can the national quality forum tell us about performance measurement in anesthesiology?. Anesth Analg 2015; 120 (02) 440-448
    CrossrefPubMedGoogle Scholar
  • 19 Palmer RC, Ismond D, Rodriquez EJ, Kaufman JS. Social determinants of health: Future directions for health disparities research. Am J Public Health 2019; 109 (S1): S70-S71
    CrossrefPubMedGoogle Scholar
  • 20 Bazemore AW, Cottrell EK, Gold R. , et al. “Community vital signs”: incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Inform Assoc 2016; 23 (02) 407-412
    CrossrefPubMedGoogle Scholar
  • 21 Kheterpal S. Clinical research using an information system: the multicenter perioperative outcomes group. Anesthesiol Clin 2011; 29 (03) 377-388
    CrossrefPubMedGoogle Scholar
  • 22 Gan TJ, Diemunsch P, Habib AS. , et al; Society for Ambulatory Anesthesia. Consensus guidelines for the management of postoperative nausea and vomiting. Anesth Analg 2014; 118 (01) 85-113
    CrossrefPubMedGoogle Scholar
  • 23 Junger A, Hartmann B, Benson M. , et al. The use of an anesthesia information management system for prediction of antiemetic rescue treatment at the postanesthesia care unit. Anesth Analg 2001; 92 (05) 1203-1209
    CrossrefPubMedGoogle Scholar
  • 24 Shwartz M, Restuccia JD, Rosen AK. Composite measures of health care provider performance: a description of approaches. Milbank Q 2015; 93 (04) 788-825
    CrossrefPubMedGoogle Scholar
  • 25 Maman S, Andreae M. An EMR-derived individual anesthesia provider metric to measure risk-adjusted adherence with perioperative prophylactic bundles for health systems science and disparity research. Paper presented at: Annual Meeting of the Association of University Anesthesiologists; May 16–20, 2019; Montreal, Canada
    PubMedGoogle Scholar
  • 26 Kappen TH, Vergouwe Y, van Wolfswinkel L, Kalkman CJ, Moons KGM, van Klei WA. Impact of adding therapeutic recommendations to risk assessments from a prediction model for postoperative nausea and vomiting. Br J Anaesth 2015; 114 (02) 252-260
    CrossrefPubMedGoogle Scholar
  • 27 Biedler A, Wermelt J, Kunitz O. , et al. A risk adapted approach reduces the overall institutional incidence of postoperative nausea and vomiting. Can J Anaesth 2004; 51 (01) 13-19
    CrossrefPubMedGoogle Scholar
  • 28 Kumar A, Brampton W, Watson S, Reid VL, Neilly D. Postoperative nausea and vomiting: simple risk scoring does work. Eur J Anaesthesiol 2012; 29 (01) 57-59
    PubMedGoogle Scholar
  • 29 Rüsch D, Eberhart L, Biedler A, Dethling J, Apfel CC. Prospective application of a simplified risk score to prevent postoperative nausea and vomiting. Can J Anaesth 2005; 52 (05) 478-484
    CrossrefPubMedGoogle Scholar
  • 30 Frenzel JC, Kee SS, Ensor JE, Riedel BJ, Ruiz JR. Ongoing provision of individual clinician performance data improves practice behavior. Anesth Analg 2010; 111 (02) 515-519
    CrossrefPubMedGoogle Scholar
  • 31 Kooij FO, Klok T, Hollmann MW, Kal JE. Automated reminders increase adherence to guidelines for administration of prophylaxis for postoperative nausea and vomiting. Eur J Anaesthesiol 2010; 27 (02) 187-191
    PubMedGoogle Scholar
  • 32 Bowen DJ, Kreuter M, Spring B. , et al. How we design feasibility studies. Am J Prev Med 2009; 36 (05) 452-457
    CrossrefPubMedGoogle Scholar
  • 33 von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. ; STROBE Initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 2007; 335 (7624): 806-808
    CrossrefPubMedGoogle Scholar
  • 34 Macario A, Chung A, Weinger MB. Variation in practice patterns of anesthesiologists in California for prophylaxis of postoperative nausea and vomiting. J Clin Anesth 2001; 13 (05) 353-360
    CrossrefPubMedGoogle Scholar
  • 35 Duran D, Asada Y, Millum J, Gezmu M. Harmonizing health disparities measurement. Am J Public Health 2019; 109 (S1): S25-S27
    CrossrefPubMedGoogle Scholar
  • 36 Wickham H. Ggplot2: Elegant Graphics for Data Analysis. Vienna, Austria: R Foundation for Statistical Computing; ; New York, NY: Springer; 2016
    Google Scholar
  • 37 Wickham H, François R, Henry L, Müller K. Dplyr: A Grammar of Data Manipulation. Vienna, Austria: R Foundation for Statistical Computing; 2019
    Google Scholar
  • 38 Wickham H, Bryan J. Readxl: Read Excel Files. Vienna, Austria: R Foundation for Statistical Computing; ; New York, NY: Springer; 2019
    Google Scholar
  • 39 Xie Y. Knitr: A General-Purpose Package for Dynamic Report Generation in R. Vienna, Austria: R Foundation for Statistical Computing; ; New York, NY: Springer; 2019
    Google Scholar
  • 40 Xu HF, White RS, Sastow DL, Andreae MH, Gaber-Baylis LK, Turnbull ZA. Medicaid insurance as primary payer predicts increased mortality after total hip replacement in the state inpatient databases of California, Florida and New York. J Clin Anesth 2017; 43: 24-32
    CrossrefPubMedGoogle Scholar
  • 41 Cooper LA, Hill MN, Powe NR. Designing and evaluating interventions to eliminate racial and ethnic disparities in health care. J Gen Intern Med 2002; 17 (06) 477-486
    CrossrefPubMedGoogle Scholar
  • 42 Kaye AD, Okanlawon OJ, Urman RD. Clinical performance feedback and quality improvement opportunities for perioperative physicians. Adv Med Educ Pract 2014; 5: 115-123
    PubMedGoogle Scholar
  • 43 Maman S, Andreae M. Quantifying the impact of physician supervision on nurse anesthetists' risk-adjusted antiemetic prophylaxis. Paper presented at: Annual Meeting of the International Anesthesia Research Society; May 16–20, 2019; Montreal, Canada
    PubMedGoogle Scholar
  • 44 Kolarczyk LM, Arora H, Manning MW, Zvara DA, Isaak RS. Defining value-based care in cardiac and vascular anesthesiology: the past, present, and future of perioperative cardiovascular care. J Cardiothorac Vasc Anesth 2018; 32 (01) 512-521
    CrossrefPubMedGoogle Scholar
  • 45 Apfel CC, Bacher A, Biedler A. , et al. A factorial trial of six interventions for the prevention of postoperative nausea and vomiting [in German]. Anaesthesist 2005; 54 (03) 201-209
    CrossrefPubMedGoogle Scholar
  • 46 Macario A, Weinger M, Carney S, Kim A. Which clinical anesthesia outcomes are important to avoid? The perspective of patients. Anesth Analg 1999; 89 (03) 652-658
    PubMedGoogle Scholar
  • 47 Chung F, Mezei G. Factors contributing to a prolonged stay after ambulatory surgery. Anesth Analg 1999; 89 (06) 1352-1359
    PubMedGoogle Scholar
  • 48 Eggleton K, Liaw W, Bazemore A. Impact of gaps in merit-based incentive payment system measures on marginalized populations. Ann Fam Med 2017; 15 (03) 255-257
    CrossrefPubMedGoogle Scholar
  • 49 Anderson AC, O'Rourke E, Chin MH, Ponce NA, Bernheim SM, Burstin H. Promoting health equity and eliminating disparities through performance measurement and payment. Health Aff (Millwood) 2018; 37 (03) 371-377
    CrossrefPubMedGoogle Scholar
  • 50 Glance LG, Neuman M, Martinez EA, Pauker KY, Dutton RP. Performance measurement at a “tipping point”. Anesth Analg 2011; 112 (04) 958-966
    CrossrefPubMedGoogle Scholar
  • 51 Peccora CD, Gimlich R, Cornell RP, Vacanti CA, Ehrenfeld JM, Urman RD. Anesthesia report card - a customizable tool for performance improvement. J Med Syst 2014; 38 (09) 105
    CrossrefPubMedGoogle Scholar
  • 52 Multicenter Perioperative Outcomes Group. (2020, July 14). Postoperative Nausea and Vomiting PONV-01. ASPIRE Measures. Available at: https://spec.mpog.org/Spec/Public/24
    PubMed
  • 53 Jeyabalan S, Thampi SM, Karuppusami R, Samuel K. Comparing the efficacy of aprepitant and ondansetron for the prevention of postoperative nausea and vomiting (PONV): a double blinded, randomised control trial in patients undergoing breast and thyroid surgeries. Indian J Anaesth 2019; 63 (04) 289-294
    CrossrefPubMedGoogle Scholar
  • 54 Bartlett R, Hartle AJ. Routine use of dexamethasone for postoperative nausea and vomiting: the case against. Anaesthesia 2013; 68 (09) 892-896
    CrossrefPubMedGoogle Scholar
  • 55 McDonnell C, Barlow R, Campisi P, Grant R, Malkin D. Fatal peri-operative acute tumour lysis syndrome precipitated by dexamethasone. Anaesthesia 2008; 63 (06) 652-655
    CrossrefPubMedGoogle Scholar
  • 56 Frasier LL, Pavuluri Quamme SR, Ma Y. , et al. Familiarity and communication in the operating room. J Surg Res 2019; 235: 395-403
    CrossrefPubMedGoogle Scholar
  • 57 Sintchenko V, Magrabi F, Tipper S. Are we measuring the right end-points? Variables that affect the impact of computerised decision support on patient outcomes: a systematic review. Med Inform Internet Med 2007; 32 (03) 225-240
    CrossrefPubMedGoogle Scholar
  • 58 Kooij FO, Klok T, Hollmann MW, Kal JE. Decision support increases guideline adherence for prescribing postoperative nausea and vomiting prophylaxis. Anesth Analg 2008; 106 (03) 893-898
    CrossrefPubMedGoogle Scholar
  • 59 Simpao AF, Tan JM, Lingappan AM, Gálvez JA, Morgan SE, Krall MA. A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems. J Clin Monit Comput 2017; 31 (05) 885-894
    CrossrefPubMedGoogle Scholar
  • 60 Chien AT, Chin MH, Davis AM, Casalino LP. Pay for performance, public reporting, and racial disparities in health care: how are programs being designed?. Med Care Res Rev 2007; 64 (5, Suppl): 283S-304S
    CrossrefPubMedGoogle Scholar
  • 61 Warner DO, Isaak RS, Peterson-Layne C. , et al. Development of an objective structured clinical examination as a component of assessment for initial board certification in anesthesiology. Anesth Analg 2020; 130 (01) 258-264
    CrossrefPubMedGoogle Scholar
  • 62 Colquhoun DA, Shanks AM, Kapeles SR. , et al. Considerations for integration of perioperative electronic health records across institutions for research and quality improvement: the approach taken by the multicenter perioperative outcomes group. Anesth Analg 2020; 130 (05) 1133-1146
    CrossrefPubMedGoogle Scholar
  • 63 McCormick PJ, Yeoh C, Vicario-Feliciano RM. , et al. Improved compliance with anesthesia quality measures after implementation of automated monthly feedback. J Oncol Pract 2019; 15 (06) e583-e592
    CrossrefPubMedGoogle Scholar
  • 64 Tetzlaff JE. Assessment of competency in anesthesiology. Anesthesiology 2007; 106 (04) 812-825
    CrossrefPubMedGoogle Scholar
  • 65 Kooij FO, Klok T, Preckel B, Hollmann MW, Kal JE. The effect of requesting a reason for non-adherence to a guideline in a long running automated reminder system for PONV prophylaxis. Appl Clin Inform 2017; 8 (01) 313-321
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 66 Hoberman J. Medical racism and the rhetoric of exculpation: how do physicians think about race?. New Lit Hist 2007; 38 (03) 505-525
    CrossrefPubMedGoogle Scholar
  • 67 Andreae MH, Nair S, Gabry JS, Goodrich B, Hall C, Shaparin N. A pragmatic trial to improve adherence with scheduled appointments in an inner-city pain clinic by human phone calls in the patient's preferred language. J Clin Anesth 2017; 42: 77-83
    CrossrefPubMedGoogle Scholar
  • 68 Shaparin N, White R, Andreae M, Hall C, Kaufman A. A longitudinal linear model of patient characteristics to predict failure to attend an inner-city chronic pain clinic. J Pain 2014; 15 (07) 704-711
    CrossrefPubMedGoogle Scholar
  • 69 Payne VL, Hysong SJ. Model depicting aspects of audit and feedback that impact physicians' acceptance of clinical performance feedback. BMC Health Serv Res 2016; 16: 260
    CrossrefPubMedGoogle Scholar
  • 70 Carpenter B, Gelman A, Hoffman M. , et al. Stan: a probabilistic programming language. J Stat Softw 2017; 76 (01) 1-32
    PubMedGoogle Scholar
  • 71 Bürkner P-C. Advanced Bayesian multilevel modeling with the R package brms. R J 2018; 10 (01) 395-411
    CrossrefPubMedGoogle Scholar