column-alafaireet-may09

In many ways, the health care industry and the banking and financial services industries share common clients and provide similar services. Each is concerned with the well being of its clients and the provision of safe, secure essential services designed to enhance that well-being. While health care is more newly focused on the provision of client centered services in addition to excellent clinical care, banking has a longer history of customer focused services. Banking and financial service providers can, if they are willing to share their expertise, provide useful tools and insights that are immediately translatable into the provision of quality health care.

Banking and financial services providers are currently focused on supporting health care through enhanced financial services such a rapid physician reimbursement, and the provision of secure platforms for data and information transfer. There are, quite possible, a number of other opportunities that the banking and financial services industries might wish to consider. For example, the use of credit scores to determine risk when patients are paying their own health care costs rather than those costs being paid for through a third party payer has positively affected the bottom line of health care providers who take advantage of those scores. But the use of credit scores to assess the risk of non-payment for medical services rendered isn’t the only, or even perhaps the best use of, those scores. Both banking and health care could better use those scores if they were to use them in an innovative way to address the issue of health care visit non-adherence.

Visit non-adherence in health care costs the US health care system annually[1]. It also costs untold amounts of needless human suffering and creates a vicious cycle of increased health care costs and decreased clinical quality [2]. When patients and physicians don’t meet for planned visits, subsequent medical care tends to be of higher intensity and higher cost[3]. So why don’t patients simply show up for their appointments? Past research into visit adherence has focused either on the identification of specific patient social – economic characteristics that potentially predict non-adherent behavior or focused on the utilization of redundant scheduling techniques [4, 5]. Neither of these strategies works particularly well, so the whole issue of visit non-adherence has come to be seen as an intractable problem.

So, should banking and financial services providers be interested in visits non-adherence? Aside from the potential for that $100 billion to be used in more profitable ways, the use of credit scores may be extendable to address health care visit adherence. Research at the University of Missouri has preliminarily concluded that visit non-adherence is predicable if it is considered as a set of circumstances, rather than a “bad patient” or “bad schedule” [6]. If a visit is looked at as set of determinants that include not only patient social-economic factors, but also clinical diagnosis factors and logistical factors[7], the ability to predict if a visit will occur before it is scheduled becomes a reality. The list of determinants that appears to have the most predictive value for the likelihood of visit non-adherence bears a very strong resemblance to the determinants use to calculate credit scores. Some determinants, such as client/patient age and gender, are identical in the determination of visit adherence and credit scoring [8, 9]. Other determinants, such a past appointment keeping behavior align very closely with elements of credit scoring such a past credit history. Even elements that measure the relative stability of the client/patient, such a length of employment, ownership of home, and length of residence in current home have corollaries in health care in employment status, type of appointment, and use of certain types of medical services providers. Of the 23 potential determinants of visit non-adherence [10], the information collected for credit scoring is identical or very closely related in 14 instances [11, 12]. All three of the top determinants of health care visit adherence are collected as part of the credit scoring process.

The study at the University of Missouri used general information about the calculation of credit scores rather than the exact proprietary algorithms, but it appears that these algorithms may in fact, be useful, with some adaptation, in the predication of visit non-adherence and the resolution of an expensive, seemingly intractable, issue in the delivery of high quality health care. The future may hold other innovative applications for the tools developed in the financial world to the provision of affordable, easily accessible health care

1. Vermeire, E., et al. 2001. Patient adherence to treatment: three decades of research. A comprehensive review. J Clin Pharm Ther. 26: 331-342.

3. Ulmer, T., Troxler, C. 2004. The Economic Cost of Missed Appointments and the Open Access System. Community Health Scholars.

4. Martin, C., T. Perfect & G. Mantle. 2005. Non-attendance in primary care: the views of patients and practices on its causes, impact and solutions. Family Practice. 22: 638-643.

5. George, A. & G. Rubin. 2003. Non-attendance in general practice: a systematic review and its implications for access to primary health care. Fam. Pract. 20: 178-184.

6. Alafaireet, P. & H. Houghton. 2008. Get Your Share of 100 Billion Dollars in Lost Revenue- Leverage the Practice Tools You Have and Reduce No-Show Visits by 83%+ Presented at MGMA Annual Conference.

7. Lacy, N. L., et al. 2004. Why we don't come: patient perceptions on no-shows. Annals of Family Medicine. 2: 541-545.

8. Smith, C. M. & B. P. Yawn. 1994. Factors associated with appointment keeping in a family practice residency clinic. Journal of Family Practice. 38: 25-29.

9. Program, M. H. B. A. How Credit Scores Work, How a Score is Calculated, Vol. 2008.

10. Gruzd, D. C., C. L. Shear & W. M. Rodney. 1986. Determinants of no-show appointment behavior: the utility of multivariate analysis. Family Medicine. 18: 217-220.

11. Staff, B. 2006. Factors that Affect Your Credit Score, Vol. 2008. BrightScore, Ed.: Factors in a credit score. Young Money.

12. CreditInfocenter. 1999. Credit scores and what influences them, according to Fair Isaac, Vol.2008.

>> What's your point of view? Email Dr. HSA. We look forward to hearing from you!

Patricia Alafaireet, works in the department of Health Management and informatics (in the School of Medicine at the University of Missouri) as the Director of Applied Health Informatics. My PhD work, however, is through the University of Missouri Informatics institute.