2001, p.160).
Results also indicated that patients between the ages of forty-six and sixty-four incurred significantly larger financial losses for the facility than the rest of the patients and Medicare and Medicaid nonexempt patients admitted via routine admission had the highest average loss for inpatient visits. Also, on Tuesdays, the average loss per patient was significantly greater for patients in the region. Only with data-mining could such an apparently insignificant finding as the predominance of Tuesday admittances come to light. The reason for this was not random: "Examination of Tuesday's admitting physicians" revealed that "several of the physicians were in the same medical specialty. This specialty cared for patients that typically required a high level of service intensity over a long period of time. The identification of a subset of patients with disproportionately high costs has prompted the institution to reevaluate its admission criteria to this unit" and also the diagnostic approach of the physicians in...
p. 160).
One potentially negative effect of this case study is that it merely targets which patients are most costly to the system, rather than seeks to provide solutions for dispensing effective care at a lower cost. While this case study did bring to light financially inefficient decision-making criteria on the part of a specific group of physicians, data mining does not necessarily produce superior care or offer a solution, other than to reduce the intake of these specific patients. This raises ethical questions about discrimination as a method of cost control and more analysis may be needed to ensure that reforms are undertaken in a compassionate and sensitive manner.
References
Silver, Michael. Taiki Sakata; Hua-Ching Su; Charles Herman; Steven B. Dolins; & Michael J. O'Shea. (2001, Summer). "Case study: How to apply data mining techniques in a healthcare data warehouse." Journal of Healthcare Information Management. 15(2). Retrieved June 5, 2010 at…
