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Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers

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Abstract

Patient-centered appointment access is of critical importance at community health centers (CHCs) and its optimal implementation entails the use of advanced data analytics. This study seeks to optimize patient-centered appointment scheduling through data mining of Electronic Health Record/Practice Management (EHR/PM) systems. Data was collected from different EHR/PM systems in use at three CHCs across the state of Indiana and integrated into a multidimensional data warehouse. Data mining was performed using decision tree modeling, logistic regression, and visual analytics combined with n-gram modeling to derive critical influential factors that guide implementation of patient-centered open-access scheduling. The analysis showed that appointment adherence was significantly correlated with the time dimension of scheduling, with lead time for an appointment being the most significant predictor. Other variables in the time dimension such as time of the day and season were important predictors as were variables tied to patient demographic and clinical characteristics. Operationalizing the findings for selection of open-access hours led to a 16% drop in missed appointment rates at the interventional health center. The study uncovered the variability in factors affecting patient appointment adherence and associated open-access interventions in different health care settings. It also shed light on the reasons for same-day appointment through n-gram-based text mining. Optimizing open-access scheduling methods require ongoing monitoring and mining of large-scale appointment data to uncover significant appointment variables that impact schedule utilization. The study also highlights the need for greater “in-CHC” data analytic capabilities to re-design care delivery processes for improving access and efficiency.

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References

  1. Hurtado MP, Swift EK, Corrigan JM (2001) Crossing the quality chasm: a new health system for the 21st century. Institute of Medicine, Committee on the National Quality Report on Health Care Delivery

  2. Pitkin Derose K, Varda DM (2009) Social capital and health care access: a systematic review. Med Care Res Rev 66(3):272–306

  3. Litaker D, Koroukian SM, Love TE (2005) Context and health care access: looking beyond the individual. Med Care 43(6):531–540

    Article  Google Scholar 

  4. Patient-Centered Medical Home. https://pcmh.ahrq.gov/. Accessed 24 Jan 2017

  5. Health care under the Affordable Care Act. http://www.hhs.gov/health care/. Accessed 24 Jan 2017

  6. Carrier E, Gourevitch MN, Shah NR (2009) Medical homes: challenges in translating theory into practice. Med Care 47(7):714–722

    Article  Google Scholar 

  7. Friedman CP, Wong AK, Blumenthal D (2010) Achieving a nationwide learning health system. Sci Transl Med 2(57):57cm29–57cm29

    Article  Google Scholar 

  8. Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37

    Google Scholar 

  9. Phan K, Brown SR (2009) Decreased continuity in a residency clinic: a consequence of open access scheduling. Fam Med 41(1):46–50

    Google Scholar 

  10. Robinson LW, Chen RR (2010) A comparison of traditional and open-access policies for appointment scheduling. M&SOM 12(2):330–346

  11. LaGanga LR, Lawrence SR (2007) Clinic overbooking to improve patient access and increase provider productivity. Decis Sci 38(2):251–276

    Article  Google Scholar 

  12. Murray M, Berwick DM (2003) Advanced access: reducing waiting and delays in primary care. JAMA 289(8):1035–1040

    Article  Google Scholar 

  13. Forjuoh SN, Averitt WM, Cauthen DB, Couchman GR, Symm B, Mitchell M (2001) Open-access appointment scheduling in family practice: comparison of a demand prediction grid with actual appointments. J Am Board Fam Pract 14(4):259–265

    Google Scholar 

  14. Ulmer T, Troxler C (2006) The economic cost of missed appointments and the open access system. Community Health Scholars

  15. Murray M, Bodenheimer T, Rittenhouse D, Grumbach K (2003) Improving timely access to primary care: case studies of the advanced access model. JAMA 289(8):1042–1046

    Article  Google Scholar 

  16. Parente DH, Pinto MB, Barber JC (2005) A pre-post comparison of service operational efficiency and patient satisfaction under open access scheduling. Health Care Manag Rev 30(3):220–228

    Article  Google Scholar 

  17. Kaplan G, Lopez MH, McGinnis JM (2015) Transforming health care scheduling and access: Getting to now. Washington DC: Institute of Medicine

  18. Rose KD, Ross JS, Horwitz LI (2011) Advanced access scheduling outcomes: a systematic review. Arch Intern Med 171(13):1150–1159

    Article  Google Scholar 

  19. Cashman SB, Savageau JA, Lemay CA, Ferguson W (2004) Patient health status and appointment keeping in an urban community health center. J Health Care Poor Underserved 15(3):474–488

    Article  Google Scholar 

  20. Huang Y, Hanauer DA (2014) Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Informatics 5(3):836–860

    Article  Google Scholar 

  21. Odonkor CA, Christiansen S, Chen Y, Sathiyakumar A, Chaudhry H, Cinquegrana D, Lange J, He C, Cohen SP (2017) Factors associated with missed appointments at an academic pain treatment center: a prospective year-long longitudinal study. Anesth Analg 125(2):562–570

    Article  Google Scholar 

  22. Samorani M, LaGanga LR (2015) Outpatient appointment scheduling given individual day-dependent no-show predictions. Eur J Oper Res 240(1):245–257

    Article  MathSciNet  MATH  Google Scholar 

  23. Daggy J, Lawley M, Willis D, Thayer D, Suelzer C, DeLaurentis PC, Turkcan A, Chakraborty S, Sands L (2010) Using no-show modeling to improve clinic performance. Health Informatics J 16(4):246–259

    Article  Google Scholar 

  24. Harris SL, May JH, Vargas LG (2016) Predictive analytics model for healthcare planning and scheduling. Eur J Oper Res 253(1):121–131

    Article  MathSciNet  MATH  Google Scholar 

  25. Goldman L, Freidin R, Cook EF, Eigner J, Grich P (1982) A multivariate approach to the prediction of no-show behavior in a primary care center. Arch Intern Med 142(3):563–567

    Article  Google Scholar 

  26. Bennett KJ, Baxley EG (2009) The effect of a carve-out advanced access scheduling system on no-show rates. Fam Med 41(1):51–56

    Google Scholar 

  27. Wright MD, Flanagan ME, Kunjan K, Doebbeling BN, Toscos T (2016) Missing links: challenges in engaging the underserved with health information and communication technology. In Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare (pp. 122–129). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

  28. Toscos T, Wright MD, Flanagan ME, Kunjan K, Olson-Miller A, Doebbeling BN (2017) Tailored, theory-based strategies for engaging lowincome populations with a personal health record. EAI Endorsed Transactions on Pervasive Health and Technology 3. 152885. https://doi.org/10.4108/eai.13-7-2017.152885

  29. Toscos T, Carpenter M, Flanagan M, Kunjan K, Doebbeling BN (2018) Identifying successful practices to overcome access to care challenges in community health centers: a “positive deviance” approach. Health Serv Res Manag Epidemiol 5, 2333392817743406

  30. Kunjan K, Toscos T, Turkcan A, Doebbeling BN (2015) A multidimensional data warehouse for community health centers. In AMIA Annual Symposium Proceedings (Vol. 2015, p. 1976). American Medical Informatics Association

  31. Kunjan K, Doebbeling B, Toscos T (2018) Dashboards to support operational decision making in health centers: a case for role-specific design. Int J Hum Comput Interact 1–9.

  32. RapidMiner Data Science Platform. https://rapidminer.com/. Accessed 15 Mar 2018

  33. The R-Project for Statistical Computing. https://www.r-project.org/. Accessed 15 Mar 2018

  34. Tableau Software for Business Intelligence and Analytics http://www.tableau.com/. Accessed 15 Mar 2018

  35. Sikka R, Morath JM, Leape L (2015) The Quadruple Aim: care, health, cost and meaning in work BMJ Quality and Safety; 24:608–610

  36. Parikh A, Gupta K, Wilson AC, Fields K, Cosgrove NM, Kostis JB (2010) The effectiveness of outpatient appointment reminder systems in reducing no-show rates. Am J Med 123(6):542–548

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank and acknowledge PCORI (Patient-Centered Outcomes Research Institute), which has funded this project [IH-12-11-5488; “Improving Access to Care and Efficiency of Healthcare Systems for Underserved Patients”]. The authors would also like to acknowledge the staff at the collaborating health centers for their time and support, and the Indiana University School of Informatics and Computing (Indianapolis, IN) for providing the necessary technology infrastructure.

Funding

This study was funded by the Patient-Centered Outcomes Research Institute (Award IH-12-11-5488).

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Correspondence to Kislaya Kunjan.

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Kunjan, K., Wu, H., Toscos, T.R. et al. Large-Scale Data Mining to Optimize Patient-Centered Scheduling at Health Centers. J Healthc Inform Res 3, 1–18 (2019). https://doi.org/10.1007/s41666-018-0030-0

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