The proper care of medical patients is essential for optimal treatment of their medical conditions. Typically, a patient having a particular condition/ailment is prescribed a medicine or treatment based upon established treatment guidelines. The treatment guidelines outline, inter alia, the specific dosages of medicines, the frequency in which dosages should be administered, instructions on how dosages should be administered and the time-lines for therapeutic treatments. Oftentimes, treatment for new patients is administered directly from the treatment guidelines with little variation. These guidelines are typically derived through prospective medical studies. Prospective medical studies, namely, randomized clinical trials, are studies wherein researchers empirically test hypotheses in near ideal conditions by screening the patient population, ensuring that patient care diligently follows the guidelines and recording all relevant data. Such practices fail to take full advantages of historical medical data, rather, relying only on success rates for the patients that rigidly adhered to the treatment guidelines. Additionally, clinical trials are very expensive to conduct.
Historical medical data represents a valuable source in the analysis of the patient care process and medical outcomes. As indicated, treatment guidelines have been generated based solely upon the results of treatment on patients who rigidly adhered to the treatment guidelines. However, the number of variables from patient and professional medical care having an impact on the results of patient care is exceedingly high. Moreover, the relationship between these variables is virtually unknown. Accordingly, the ability to fully learn from past medical data could greatly improve patient health care.
Retrospective studies, for example, the analysis of historical medical patient records from a hospital, are complementary to prospective clinical trials. Health-care organizations are accumulating vast stores of patient data, which are a vital tool for knowledge management. Analyzing this already-collected information may lead to insights that can be subsequently verified in a prospective trial. Most importantly, retrospective studies can measure, in a least two ways, the impact of guidelines in real-life clinical settings. First, retrospective studies can determine the effectiveness of the treatment for a patient population that was excluded from clinical trials. For example, patients above 65, or those with other diseases may be excluded in a clinical trial—however, the guideline validated in that trial is now used to treat all hospital patients. Second, patient treatment in a hospital may differ from that in a trial. For instance, the colon cancer guideline mandates commencing chemotherapy within 6 weeks of surgery, which is rigorously enforced in the clinical trial. However, in a hospital, some patients may begin chemotherapy up to 10 weeks after surgery (e.g., they may be too sick or miss appointments). The impact of this delay on a patient's outcome can only be determined via retrospective analysis since it is not ethical to conduct a clinical trial that would test the impact of this delay—in effect, withholding the accepted standard of care.
However, analyzing hospital data is hard for many reasons. First, medical data is very complex to analyze because of its rich structure. Many traditional statistical methods are ill-suited to data with structure, time-sequenced events (medical data has important temporal components) and/or no structure such as free text, images, etc. Second, because the hospital patient data was collected to treat the patient (as opposed to collected for analysis in a clinical trial), it is imperfect in many ways, for example, missing/incorrect/inconsistent data; key outcomes/variables not recorded; bias in data collection, e.g., sick patients get more tests than well ones, (this is perfectly natural from the medical point of view, but has inherent assumptions that may cause problems for many algorithms); and variables collected/treatments change over time, which particularly impacts some long-term diseases whose treatment can span decades. Lastly, there is wide variation in practice among medical professionals determining if a patient is on a guideline and treated properly is difficult to tell.
In view of the above, there exists a need for techniques to collect population-based patient information from a variety of sources, to perform outcome analysis on the collected information, and to conduct retrospective analysis on a large quantity of medical information derived from various sources in a rapid manner.