Development and validation of nonattendance predictive models for scheduled adult outpatient appointments in different medical specialties.
appointments
nonattendance
predictive model
Journal
The International journal of health planning and management
ISSN: 1099-1751
Titre abrégé: Int J Health Plann Manage
Pays: England
ID NLM: 8605825
Informations de publication
Date de publication:
Mar 2023
Mar 2023
Historique:
revised:
07
10
2022
received:
06
07
2021
accepted:
17
10
2022
pubmed:
4
11
2022
medline:
15
3
2023
entrez:
3
11
2022
Statut:
ppublish
Résumé
Nonattendance is a critical problem that affects health care worldwide. Our aim was to build and validate predictive models of nonattendance in all outpatients appointments, general practitioners, and clinical and surgical specialties. A cohort study of adult patients, who had scheduled outpatient appointments for General Practitioners, Clinical and Surgical specialties, was conducted between January 2015 and December 2016, at the Italian Hospital of Buenos Aires. We evaluated potential predictors grouped in baseline patient characteristics, characteristics of the appointment scheduling process, patient history, characteristics of the appointment, and comorbidities. Patients were divided between those who attended their appointments, and those who did not. We generated predictive models for nonattendance for all appointments and the three subgroups. Of 2,526,549 appointments included, 703,449 were missed (27.8%). The predictive model for all appointments contains 30 variables, with an area under the ROC (AUROC) curve of 0.71, calibration-in-the-large (CITL) of 0.046, and calibration slope of 1.03 in the validation cohort. For General Practitioners the model has 28 variables (AUROC of 0.72, CITL of 0.053, and calibration slope of 1.01). For clinical subspecialties, the model has 23 variables (AUROC of 0.71, CITL of 0.039, and calibration slope of 1), and for surgical specialties, the model has 22 variables (AUROC of 0.70, CITL of 0.023, and calibration slope of 1.01). We build robust predictive models of nonattendance with adequate precision and calibration for each of the subgroups.
Types de publication
Journal Article
Langues
eng
Pagination
377-397Informations de copyright
© 2022 John Wiley & Sons Ltd.
Références
Giunta D, Briatore A, Baum A, Luna D, Waisman G, de Quiros FGB. Factors associated with nonattendance at clinical medicine scheduled outpatient appointments in a university general hospital. Patient Prefer Adherence. 2013;7:1163-1170. https://doi.org/10.2147/ppa.s51841
Giunta DH, Serena MA, Luna D, et al. Association between non-attendance to outpatient clinics and emergency department consultations, hospitalizations and mortality in a Health Maintenance Organization. Int J Health Plan Manag. 2020;35(5):1140-1156. https://doi.org/10.1002/hpm.3021
Kheirkhah P, Feng Q, Travis LM, Tavakoli-Tabasi S, Sharafkhaneh A. Prevalence, predictors and economic consequences of no-shows. BMC Health Serv Res. 2016;16(1):13. https://doi.org/10.1186/s12913-015-1243-z
Berg BP, Murr M, Chermak D, et al. Estimating the cost of no-shows and evaluating the effects of mitigation strategies. Med Decis Mak. 2013;33(8):976-985. https://doi.org/10.1177/0272989x13478194
Jabalera Mesa ML, Morales Asencio JM, Rivas Ruiz F, Porras González MH. [Analysis of economic cost of missed outpatient appointments]. Rev Calid Asist. 2017;32(4):194-199. https://doi.org/10.1016/j.cali.2017.01.004
George A, Rubin G. Non-attendance in general practice: a systematic review and its implications for access to primary health care. Fam Pract. 2003;20(2):178-184. https://doi.org/10.1093/fampra/20.2.178
Wolff DL, Waldorff FB, von Plessen C, et al. Rate and predictors for non-attendance of patients undergoing hospital outpatient treatment for chronic diseases: a register-based cohort study. BMC Health Serv Res. 2019;19(1):1-11. https://doi.org/10.1186/s12913-019-4208-9
Karter AJ, Parker MM, Moffet HH, et al. Missed appointments and poor glycemic control: an opportunity to identify high-risk diabetic patients. Med Care. 2004;42(2):110-115. https://doi.org/10.1097/01.mlr.0000109023.64650.73
Lee RRS, Samsudin MI, Thirumoorthy T, Low LL, Kwan YH. Factors affecting follow-up non-attendance in patients with Type 2 diabetes mellitus and hypertension: a systematic review. Singap Med J. 2019;60(5):216-223. https://doi.org/10.11622/smedj.2019042
Colubi MM, Pérez-Elías MJ, Elías L, et al. Missing scheduled visits in the outpatient clinic as a marker of short-term admissions and death. HIV Clin Trials. 2012;13(5):289-295. https://doi.org/10.1310/hct1305-289
Srisaenpang S, Pinitsoontorn S, Singhasivanon P, et al. Missed appointments at a tuberculosis clinic increased the risk of clinical treatment failure. Southeast Asian J Trop Med Publ Health. 2006;37(2):345-350.
Turkcan A, Nuti L, DeLaurentis PC, et al. No-show modeling for adult ambulatory clinics. In: Handbook of Healthcare Operations Management; 2013:251-288. https://doi.org/10.1007/978-1-4614-5885-2_10
Giunta DH, Alonso Serena M. Nonattendance rates of scheduled outpatient appointments in a university general hospital. Int J Health Plann Manag. 2019;34(4):1377-1385. https://doi.org/10.1002/hpm.2797
Neal RD, Hussain-Gambles M, Allgar VL, Lawlor DA, Dempsey O. Reasons for and consequences of missed appointments in general practice in the UK: questionnaire survey and prospective review of medical records. BMC Fam Pract. 2005;6(1):47. https://doi.org/10.1186/1471-2296-6-47
Nancarrow S, Bradbury J, Avila C. Factors associated with non-attendance in a general practice super clinic population in regional Australia: a retrospective cohort study. Australas Med J. 2014;7(8):323-333. https://doi.org/10.4066/amj.2014.2098
Neal RD, Lawlor DA, Allgar V, et al. Missed appointments in general practice: retrospective data analysis from four practices. Br J Gen Pract. 2001;51(471):830-832.
Frankel S, Farrow A, West R. Non-attendance or non-invitation? a case-control study of failed outpatient appointments. BMJ. 1989;298(6684):1343-1345. https://doi.org/10.1136/bmj.298.6684.1343
Hamilton W, Round A, Sharp D. Patient, hospital, and general practitioner characteristics associated with non-attendance: a cohort study. Br J Gen Pract. 2002;52(477):317-319.
Leung GM, Castan-Cameo S, McGhee SM, Wong IOL, Johnston JM. Waiting time, doctor shopping, and nonattendance at specialist outpatient clinics: case-control study of 6495 individuals in Hong Kong. Med Care. 2003;41(11):1293-1300. https://doi.org/10.1097/01.mlr.0000093481.93107.c2
Bickler CB. Defaulted appointments in general practice. J Roy Coll Gen Pract. 1985;35(270):19-22.
Nguyen DL, Dejesus RS, Wieland ML. Missed appointments in resident continuity clinic: patient characteristics and health care outcomes. J Grad Med Educ. 2011;3(3):350-355. https://doi.org/10.4300/jgme-d-10-00199.1
Richardson WP, Higgins AC, Ames RG. Rates of attendance and reasons for nonattendance at a clinic of handicapping conditions. Am J Public Health Nation's Health. 1964;54(8):1177-1183. https://doi.org/10.2105/ajph.54.8.1177
Goldman L. A multivariate approach to the prediction of no-show behavior in a primary care center. Arch Intern Med. 1982;142(3):563-567. https://doi.org/10.1001/archinte.142.3.563
Stubbs ND, Sanders S, Jones DB, Geraci SA, Stephenson PL. Methods to reduce outpatient non-attendance. Am J Med Sci. 2012;344(3):211-219. https://doi.org/10.1097/maj.0b013e31824997c6
Moine JM, Bigatti CG, Leale G, Carnevali G, Francheli E. Un modelo predictivo para reducir la tasa de ausentismo en atenciones médicas programadas. Accessed 15 June 2018. http://42jaiio.sadio.org.ar/proceedings/simposios/Trabajos/CAIS/20.pdf
Creps J, Lotfi V. A dynamic approach for outpatient scheduling. J Med Econ. 2017;20(8):786-798. https://doi.org/10.1080/13696998.2017.1318755
Elvira C, Ochoa A, Gonzalvez JC, Mochon F. Machine-learning-Based No show prediction in outpatient visits. Int J Artif Intell. 2018;4(7):29. https://doi.org/10.9781/ijimai.2017.03.004
Huang Y, Hanauer DA. Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inf. 2014;5(3):836-860. https://doi.org/10.4338/aci-2014-04-ra-0026
Huang YL, Hanauer DA. Time dependent patient no-show predictive modelling development. Int J Health Care Qual Assur. 2016;29(4):475-488. https://doi.org/10.1108/ijhcqa-06-2015-0077
Harvey HB, Benjamin Harvey H, Liu C, et al. Predicting No-shows in radiology using regression modeling of data available in the electronic medical record. J Am Coll Radiol. 2017;14(10):1303-1309. https://doi.org/10.1016/j.jacr.2017.05.007
Kurasawa H, Hayashi K, Fujino A, et al. Machine-learning-Based prediction of a missed scheduled clinical appointment by patients with diabetes. J Diabetes Sci Technol. 2016;10(3):730-736. https://doi.org/10.1177/1932296815614866
Carreras-García D, Delgado-Gómez D, Llorente-Fernández F, Arribas-Gil A. Patient No-show prediction: a systematic literature review. Entropy. 2020;22(6):675. https://doi.org/10.3390/e22060675
Rubinstein A, Zerbino MC, Cejas C, López A. Making universal health care effective in Argentina: a blueprint for reform. Health Syst Reform. 2018;4(3):203-213. https://doi.org/10.1080/23288604.2018.1477537
SNOMED International. Accessed May 28, 2017. http://www.snomed.org/snomed-ct
Franco M, Giussi Bordoni MV, Otero C, et al. Problem oriented medical record: characterizing the use of the problem list at hospital Italiano de Buenos Aires. Stud Health Technol Inf. 2015;216:877.
Luna D, Franco M, Plaza C, et al. Accuracy of an electronic problem list from primary care providers and specialists. Stud Health Technol Inf. 2013;192:417-421.
Plazzotta F, Otero C, Luna D, de Quiros FGB. Natural language processing and inference rules as strategies for updating problem list in an electronic health record. Stud Health Technol Inf. 2013;192:1163.
Google Maps Distance Matrix API | Google Developers. Google Developers. Accessed June 13, 2017. https://developers.google.com/maps/documentation/distance-matrix/?hl=es
Geographical Distance - Wikipedia. Accessed June 2, 2017. https://en.wikipedia.org/wiki/Geographical_distance
Geodesics on an Ellipsoid - Wikipedia. Accessed June 2, 2017. https://en.wikipedia.org/wiki/Geodesics_on_an_ellipsoid
Distance Calculation Algorithms. Australia Government. Geoscience Australia. Published May 15, 2014. Accessed June 19, 2017. http://www.ga.gov.au/scientific-topics/positioning-navigation/geodesy/geodetic-techniques/distance-calculation-algorithms
Primeros Pasos | Google Maps Geocoding API | Google Developers. Google Developers. Accessed June 13, 2017. https://developers.google.com/maps/documentation/geocoding/start?hl=es
Servicio Meteorológico Nacional. Accessed June 2, 2017. http://www.smn.gov.ar/
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40(5):373-383. https://doi.org/10.1016/0021-9681(87)90171-8
Katz MH. Multivariable Analysis: A Practical Guide for Clinicians and Public Health Researchers. Cambridge University Press; 2011.
Chaibub Neto E, Pratap A, Perumal TM, et al. Detecting the impact of subject characteristics on machine learning-based diagnostic applications. NPJ Digit Med. 2019;2(1):99. https://doi.org/10.1038/s41746-019-0178-x
Han K, Song K, Choi BW. How to develop, validate, and compare clinical prediction models involving radiological parameters: study design and statistical methods. Korean J Radiol. 2016;17(3):339-350. https://doi.org/10.3348/kjr.2016.17.3.339
Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128-138. https://doi.org/10.1097/ede.0b013e3181c30fb2
Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J. 2014;35(29):1925-1931. https://doi.org/10.1093/eurheartj/ehu207
Servicio Meteorológico Nacional. Accessed May 26, 2018. https://www.smn.gob.ar/
Devasahay SR, Karpagam S, Ma NL. Predicting appointment misses in hospitals using data analytics. mHealth. 2017;3:12. https://doi.org/10.21037/mhealth.2017.03.03
Harrell FE, Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361-387. https://doi.org/10.1002/(sici)1097-0258(19960229)15:4<361::aid-sim168>3.0.co;2-4
Briatore A, Tarsetti EV, Latorre A, et al. Causes of appointment attendance, nonattendance, and cancellation in outpatient consultations at a university hospital. Int J Health Plann Manag. 2020;35(1):207-220. https://doi.org/10.1002/hpm.2890
Lawrence K, Nov O, Mann D, Mandal S, Iturrate E, Wiesenfeld B. Virtual work outside work: the impact of telemedicine on physicians’ after-hours EHR work during the COVID-19 pandemic. JMIR Med Inf. 2022;10(7):e34826. https://doi.org/10.2196/34826
Nayyar D, Pendrith C, Kishimoto V, et al. Quality of virtual care for ambulatory care sensitive conditions: patient and provider experiences. Int J Med Inf. 2022;165:104812. https://doi.org/10.1016/j.ijmedinf.2022.104812
Hsieh YP, Yen CF, Wu CF, Wang PW. Nonattendance at scheduled appointments in outpatient clinics due to COVID-19 and related factors in Taiwan: a health belief model approach. Int J Environ Res Publ Health. 2021;18(9):4445. https://doi.org/10.3390/ijerph18094445