Ahmad, M Usman

Mhaskar, Rahul, MPH, PhD

Zhang, Angie

English

Lehigh Valley Health Network: LVHN Scholarly Works

Lehigh Valley Health NetworkLVHN Scholarly WorksUSF-LVHN SELECTA Predictive Model for Decreasing Clinical No-Show Rates in a Primary Care SettingM Usman AhmadUSF MCOM- LVHN Campus, Mohammad.Ahmad@lvhn.orgAngie ZhangUSF MCOM- LVHN Campus, Angie.Zhang@lvhn.orgRahul Mhaskar MPH, PhDFollow this and additional works at: https://scholarlyworks.lvhn.org/select-programPart of the Medical Education CommonsThis Poster is brought to you for free and open access by LVHN Scholarly Works. It has been accepted for inclusion in LVHN Scholarly Works by anauthorized administrator. For more information, please contact LibraryServices@lvhn.org.Published In/Presented AtAhmad, M. U. Zhang, A. Mhaskar, R. (2019, March). A Predictive Model for Decreasing Clinical No-Show Rates in a Primary Care Setting.Poster Presented at: 2019 SELECT Capstone Posters and Presentations Day. Kasych Family Pavilon, Lehigh Valley Health Network,Allentown, PA.© 2018 Lehigh Valley Health NetworkLehigh Valley Health Network, Allentown, PennsylvaniaIt is possible to develop a predictive model for no shows for clinics as small as single physician practices. • Some significant variables were similar to prior research, however, others differed. • Predictive modeling in conjunction with overbooking may provide an opportunity to mitigate the effect of no shows.• SELECT tools would be necessary to balance staff wellness, patient care, and revenue goalsA Predictive Model for Decreasing Clinical No-Show Rates in a Primary Care SettingREFERENCES6,758 patient visits were analyzed with probit regression.• Significant variables included: 18 to 25 years of age, 36 to 39 years of age, check up visits, no insurance, and two previous no-show visits. 3,571 patient visits were used to test the model• Model performed at 47% sensitivity and 79% specificity• Simulated predictive overbooking resulted in 3.67 vs. 6.87 unused appointments, p<0.000 (mean diff 3.2, 95% CI, 2.9 to 3.5). Visit utilization increased from 69% with normal scheduling to 82% with predictive overbooking. • The receiver operating characteristic (ROC) curve area under curve (AUC) was 0.72 (95% CI, 0.69 to 0.76) for the model and 0.70 (95% CI, 0.65 to 0.74) for predicted visits. Age, visit type, insurance status, and two previous no show visits were significant in our model. • Previous research is mixed on age and insurance status6,31• No association between no shows and race or gender which conflicts previous research19,20• Predictive modeling with overbooking has not been studied in single physician practices, but is effective21-30• Visits for chronic illness or hospital admissions have higher no show rates32• SELECT: Leadership and knowledge of health systems with technology can improve practice efficiency while emphasizing improved patient care as it relates to chronic illness and hospital follow ups. An IRB approved study was conducted to retrospectively develop a predictive model and prospectively test the model.• Staff interviews and process mapping defined the problem.• Patient visits from 2014-2015 were analyzed with regression using STATA 13• Variables included month, day, age, gender, race, ethnicity, insurance type, visit type, and number of previous no shows• A threshold for classifying no shows was determined using a histogram• The model was tested on patient visits in 2016 with sensitivity, specificity, and receiver operating characteristic (ROC) curve calculated• The model was used to simulate overbooking by visit day on the prospective sampleM. Usman Ahmad, B.A.1, Angie Zhang, B.A.1, Rahul Mhaskar, M.P.H., Ph.D.1,21. Medical Education, University of South Florida (USF) Morsani College of Medicine (MCOM), Tampa, FL2. Internal Medicine, USF MCOM, Tampa, FLBackgroundMethodsResults DiscussionConclusions1. Mäntyjärvi M. No-show patients in an ophthalmological out-patient department. Acta Ophthalmol. 1994;72(3):284-2892. Samuels RC, Ward VL, Melvin P, et al. Missed Appointments: Factors Contributing to High No-Show Rates in an Urban Pediatrics Primary Care Clinic. Clin Pediatr (Phila). 2015;54(10):976-9823. Kaplan-Lewis E, Percac-Lima S. No-show to primary care appointments: why patients do not come. J Prim Care Community Health. 2013;4(4):251-2554. Traeger L, O’Cleirigh C, Skeer MR, Mayer KH, Safren SA. Risk factors for missed HIV primary care visits among men who have sex with men. J Behav Med. 2012;35(5):548-5565. Lasser KE, Mintzer IL, Lambert A, Cabral H, Bor DH. Missed appointment rates in primary care: the importance of site of care. J Health Care Poor Underserved. 2005;16(3):475-4866. Dantas LF, Fleck JL, Cyrino Oliveira FL, Hamacher S. No-shows in appointment scheduling – a systematic literature review. Health Policy (New York). 2018;122(4):412-4217. Parikh A, Gupta K, Wilson AC, Fields K, Cosgrove NM, Kostis JB. The effectiveness of outpatient appointment reminder systems in reducing no-show rates. Am J Med. 2010;123(6):542-548. doi:10.1016/j.amjmed.2009.11.0228. Junod Perron N, Dao MD, Righini NC, et al. Text-messaging versus telephone reminders to reduce missed appointments in an academic primary care clinic: a randomized controlled trial. BMC Health Serv Res. 2013;13:125. doi:10.1186/1472-6963-13-1259. Chen Z, Fang L, Chen L, Dai H. Comparison of an SMS text messaging and phone reminder to improve attendance at a health promotion center: a randomized controlled trial. J Zhejiang Univ Sci B. 2008;9(1):34-38. doi:10.1631/jzus.B07146410. Guse CE, Richardson L, Carle M, Schmidt K. The effect of exit-interview patient education on no-show rates at a family practice residency clinic. J Am Board Fam Pract. 1997;16(5):399-404. doi:10.3122/jabfm.16.5.39911. Lesaca T. Assessing the influence of a no-show fee on patient compliance at a CMHC. Adm Policy Ment Health. 1995;22(6):629-631. doi:10.1007/BF0225482612. LaGanga LR, Lawrence SR. Clinic Overbooking to Improve Patient Access and Increase Provider Productivity. Decis Sci. 2007;38(2):251-276. doi:10.1111/j.1540-5915.2007.00158.x13. DuMontier C, Rindfleisch K, Pruszynski J, Frey JJ. A multi-method intervention to reduce no-shows in an urban residency clinic. Fam Med. 2013;45(9):634-641. http://www.ncbi.nlm.nih.gov/pubmed/24136694.14. Berg BP, Murr M, Chermak D, et al. Estimating the cost of no-shows and evaluating the effects of mitigation strategies. Med Decis Making. 2013;33(8):976-985. doi:10.1177/0272989X1347819415. Lotfi V, Torres E. Improving an outpatient clinic utilization using decision analysis-based patient scheduling. Socioecon Plann Sci. 2014;48(2):115-126. doi:10.1016/j.seps.2014.01.00216. Goffman RM, Harris SL, May JH, et al. Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health Administration. Mil Med. 2017;182(5):e1708-e1714. doi:10.7205/MILMED-D-16-0034517. Alaeddini A, Yang K, Reddy C, Yu S. A probabilistic model for predicting the probability of no-show in hospital appointments. Health Care Manag Sci. 2011;14(2):146-157. doi:10.1007/s10729-011-9148-918. Torres O, Rothberg MB, Garb J, Ogunneye O, Onyema J, Higgins T. Risk factor model to predict a missed clinic appointment in an urban, academic, and underserved setting. Popul Health Manag. 2015;18(2):131-136. doi:10.1089/pop.2014.004719. Goldman L, Freidin R, Cook EF, Eigner J, Grich P. A multivariate approach to the prediction of no-show behavior in a primary care center. Arch Intern Med. 1982;142(3):563-567. doi:10.1001/archinte.1982.0034016014302620. Cohen AD, Kaplan DM, Kraus M, Rubinshtein E, Vardy DA. Nonattendance of adult otolaryngology patients for scheduled appointments. J Laryngol Otol. 2007;121(3):258-261. doi:10.1017/S002221510600365321. Reid MW, Cohen S, Wang H, et al. Preventing patient absenteeism: validation of a predictive overbooking model. Am J Manag Care. 2015;21(12):902-910. http://www.ncbi.nlm.nih.gov/pubmed/26671702.22. Samorani M, LaGanga LR. Outpatient appointment scheduling given individual day-dependent no-show predictions. Eur J Oper Res. 2015;240(1):245-257. doi:10.1016/j.ejor.2014.06.03423. Daggy J, Lawley M, Willis D, et al. Using no-show modeling to improve clinic performance. Health Informatics J. 2010;16(4):246-259. doi:10.1177/146045821038052124. Muthuraman K, Lawley M. A stochastic overbooking model for outpatient clinical scheduling with no-shows. IIE Trans. 2008;40(9):820-837. doi:10.1080/0740817080216582325. Huang Y, Hanauer DA. Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inform. 2014;5(3):836-860. doi:10.4338/ACI-2014-04-RA-002626. Huang Y-L, Hanauer DA. Time dependent patient no-show predictive modelling development. Int J Health Care Qual Assur. 2016;29(4):475-488. doi:10.1108/IJHCQA-06-2015-007727. Zeng B, Turkcan A, Lin J, Lawley M. Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities. Ann Oper Res. 2010;178(1):121-144. doi:10.1007/s10479-009-0569-528. Creps J, Lotfi V. A dynamic approach for outpatient scheduling. J Med Econ. 2017;20(8):786-798. doi:10.1080/13696998.2017.131875529. May FP, Reid MW, Cohen S, Dailey F, Spiegel BMR. Predictive overbooking and active recruitment increases uptake of endoscopy appointments among African American patients. Gastrointest Endosc. 2017;85(4):700-705. doi:10.1016/j.gie.2016.09.00130. Reid MW, May FP, Martinez B, et al. Preventing Endoscopy Clinic No-Shows: Prospective Validation of a Predictive Overbooking Model. Am J Gastroenterol. 2016;111(9):1267-1273. doi:10.1038/ajg.2016.26931. Norris JB, Kumar C, Chand S, Moskowitz H, Shade SA, Willis DR. An empirical investigation into factors affecting patient cancellations and no-shows at outpatient clinics. Decis Support Syst. 2014;57:428-44332. Zailinawati AH, Ng CJ, Nik-Sherina H. Why do patients with chronic illnesses fail to keep their appointments? A telephone interview. Asia-Pacific J public Heal. 2006;18(1):10-15Problem StatementA challenging obstacles to primary care delivery in the United States (US) is patient no-shows or missed appointments. • No-show rate in can vary from 5.5% to 50%1-6• Loss to revenue from a no show rate of 5.5% was salary of three nursing staff1• Requires tailored prediction tool to target reduction strategies efficiently5• Solutions: phone reminders7, automated phone reminders8, text messages8,9, exit interviews10, fees11, overbooking12-14, predictive modeling15-20, and predictive modeling with overbooking21-30The clinical no-show rate is a cause of lost productivity in primary care, predictive models may help reduce the rate.Table 1: DemographicsCharacteristics Phase 1 (n = 2,946) Phase 2 (2,209)Age, years: mean (SD) 51.6 (18.6) 53.2 (18.9)Sex, male: N (%) 1744 (59.2%) 1317 (59.6%)Race, N (%)White 2677 (90.9%) 1989 (90.0%)Black 165 (5.6%) 133 (6.0%)Hispanic 48 (1.6%) 39 (1.8%)Asian 27 (0.9%) 24 (1.1%)Other 29 (1.0%) 24 (1.1%)Ethnicity, Hispanic (%) 65 (2.2%) 48 (2.2%)Insurance, N (%)Medicare 601 (20.4%) 499 (22.6%)Private Insurance 2213 (75.1%) 1634 (74.0%)Uninsured 132 (4.5%) 76 (3.4%)Table 2: Results of Probit Regression AnalysisMonth Beta Coefficient (95% CI) SignificanceNo significanceDayNo significanceAge18 to 25 0.31 (0.05 to 0.57) 0.019*36 to 49 0.26 (0.06 to 0.45) 0.012*GenderNo significanceRaceNo significanceInsuranceMedicare -0.3 (-0.6 to -0.01) 0.046*Private Insurance -0.27 (-0.51 to -0.03) 0.030*Uninsured 0 (0 to 0) ReferenceVisit TypeCU 0.39 (0.23 to 0.56) 0.000*Previous No ShowsTwice 0.94 (0.3 to 1.58) 0.004*CU = Check Up, INJ = Injection, OV = Office Visit, NP= New Patient, Physical = Physical Exam, * = significant at p <0.05Figure 2: a) A histogram plotted using Microsoft Excel with the output of the regression equation for 6,758 patient visits from 2014-2015. Visit status by show and no show is separated to highlight distributional differences. A threshold of 0.16 was chosen to classify a visit as a show or no show for model deployment. b) 251 visit days were analyzed with one excluded as holiday scheduling. The remaining visits, no shows, and overbooked appointments with predictive model were compared to maximum capacity for each visit day. Visit days are displayed as weeks with holiday weeks 23, 28, 29, 37, 48 and 53 excluded to simplify maximum capacity. c) The threshold value of 0.16 is marked on the receiver operating curve (ROC) for the training data in Microsoft Excel. The area under the curve is 0.72 (95% CI, 0.69 to 0.76). d) The threshold value of 0.16 is marked on the receiver operating curve (ROC) for the predicted data. The area under the curve is 0.70 (95% CI, 0.65 to 0.74) in Microsoft Excel.Figure 1: A process flow diagram constructed with Microsoft Visio for the single physician office with typical patient flow.

A Predictive Model for Decreasing Clinical No-Show Rates in a Primary Care Setting

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A Predictive Model for Decreasing Clinical No-Show Rates in a Primary Care Setting

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