Description:
Reference #: 1745
The University of South Carolina is offering licensing opportunities for Process for Isolating Factors Influencing Treatment Effectiveness from Electronic Medical Records
Background:
Musculoskeletal (MSK) conditions affect more than half of the U.S. population and account for 1 in 5 healthcare visits. MSK care annual costs exceed $176 billion in direct and $876 billion in indirect healthcare costs and increasing annually. Yet, there is remarkably little evidence across MSK conditions to support treatment decision making. In addition, it is acknowledgement throughout MSK literature that the comparative effects of alternative treatments often vary or are heterogeneous across patients. Consequently, for most MSK conditions the relevant policy question is not to find “the” effective treatment for all patients, but rather find the “effective mix” of treatments across patients. We define “effective mix” as the state in which no patient with an MSK condition could have switched treatments and improved their outcomes or lowered costs. Comparative effectiveness research (CER) using observational healthcare databases is needed to assess whether the treatment mix for an MSK condition in practices is effective. Unfortunately, inferences from CER are hampered by the inability in observational healthcare databases to readily and accurately measure many patient-level clinical factors thought to influence treatment effectiveness. Many of these factors are recorded in unstructured notes with patient electronic medical records which are difficult and costly to abstract by hand. CER in MSK requires efficient and valid approaches to measure the set influential clinical factors for each MSK condition from patient electronic medical records.
Invention Description:
This invention is a process for assessing the comparative effectiveness of alternative treatments by utilizing algorithms that analyze and apply data taken from electronic medical records to the set of patient clinical factors required for CER. The result is more informed guided adjustments to move current practice to toward effective treatment mix and risk-adjustments are provided for patient populations to measure provider performance.
Potential Applications:
The treatment of musculoskeletal treatments within the medical field
Advantages and Benefits:
These algorithms can be used to improve the outcomes for MSK patients while limiting spending on costly and ineffective treatments. These algorithms can also support shared decision making between providers and patients. When properly implemented within practices, providers will be able to isolate data and comparative treatment effectiveness from prior patients that are best suited to patients currently making treatment decisions.