[Successful use of healthcare data ] Describe an example of successful use of healthcare data
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1-Multiple Sclerosis: Identifying relapses in claims using an algorithm (used across all observational studies)
Using data successfully is the ultimate element to complement real-world evidence and observational research to actual practice. Since real-world research was not originally intended for research purposes, it may not collect all the clinical information however, there is value from these resources. There are new ways to measure data in order to make appropriate treatment decisions, characterize the patient population, and understand practice patterns.
Background: There are 4 phenotypes of MS however, there is only one diagnostic code, therefore it is hard to differentiate the attributable MS to the patient without a clinical diagnosis from the EMR.
The largest challenge with MS relapses where neurological disability is not recorded in a claims database however, it must be identified to understand if medications have an impact on disease progression.
This study looked at the two largest US administrative claims databases (MarketScan Commercial Claims and Encounters and Medicare Supplemental Research databases [MarketScan], and PharMetrics Plus) and the Department of Defense (DoD) administrative claims database.
– MarketScan Research databases represent health services from employees, dependents and retirees in the USA with primary or Medicare supplemental coverage through commercial health plans (multiple payers and large geoghaphic regions of US)
-PharMetrics Plus only represents patients directly through commercial health plans. (multiple payers and large geoghaphic regions of US)
-The Department of Defense (DoD) database comprises information from a single-payer healthcare system for active military personnel, their dependents and retirees. Although it is smaller than the other databases, it is the largest cradle-to-grave healthcare database in the USA. It also offers the unique advantage of containing data on mortality and, therefore, can be linked directly to the National Death Index to provide information on the cause of death for all individuals enrolled in the database. In addition, a proportion of the information in the DoD database is linked to electronic healthcare records, thus offering potential for verification of analyses by chart review.
The algorithms were able to detect the incidence of MS and distinguish newly diagnoses and MS-free years prior to diagnosis. It was also able to detect relapses occurring with or without hospitalization. The definition was further refined based on whether brain or spine MRIs were taken.
Conclusion; This study exemplifies performing clinical RWE studies in MS using a claims database. This has revolutionized the way data is interpreted and is the standard algorithm used for almost all MS claims-based studies. This increases the ability to compare data across studies and databases.
Although the primary purpose of administrative claims databases is tracking medical costs, resource use and treatment patterns, it can also be used for research in the field of MS. The focus of this research can be clinical aspects of MS, such as the occurrence of relapses, patients’ persistence with and adherence to DMTs comorbidity profiles of patients with MS. When robust and consistent methodology is applied and results are found to be in agreement with other published studies, this research can provide valuable RWE including data from patients generally not included in RCTs. In addition to expanding the evidence base for MS and its therapies, such data could also be useful for informing regulators, payers, physicians and other decision-makers regarding DMTs in a clinical practice setting outside of RCTs. It has changed the way MS is characterized in claims and all observational studies.
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This is a topic I’ve been personally interested in for a few months. I’ve done risk factor studies on ED high utilizers-for all patients and diabetes specifically. Our population findings are similar to what is in the literature. There is a bimodal age trend, meaning there are proportionally higher utilizers within two age groups, 20-29 years and 65+. Black patients and those of lower SES have a higher odds of being high utilizers, but white patients make up most of the high utilizers. Medicare and medicaid patients also have a higher odds. For diabetes patients, comorbidities played a large role with a gradient odds ratio of 2.6 for at least one comorbidity to 14.7 for four comorbidities. Next steps would be to look closer at specific groups and find their risk factors. For instance, the 20-29 year old group will have different risk factors than the 65+ group. In our population, zip code plays a huge factor for SES, so looking at groups by zip code would be beneficial.
However, I digress from the original question. I was interested in looking at the John Hopkins ACG risk stratification system and read how it has been successful. Their system can stratify patients into high, medium, and low risk tiers “based on predicted cost”. This helps clinicians focus on the right amount of care management for each tier. For instance, a patient with diabetes who is also a smoker would probably be in the highest tier and need more interventions than someone inn the lowest tier. Slough Clinical Commission group in the UK used the John Hopkins ACG to stratify and segment (where you find same group of patient who need the same intervention) to reduce ED visits. They implemented a new complex care management program and identified a group of patients who could benefit. This program involved visits every 3 weeks which were longer visits to address health behaviors related to ED visits, were “offered advice and support”. After 18 months, the results showed a reduction in ED visits by 19% and unplanned hospitalizations were down 18%.-
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