Real World Evidence/ Data (RWD) and Post Market Surveillance ( PMS) – Data collection that moves beyond clinical trials and evaluates effectiveness in widespread use of a medical product. Data used for decision making that are not collected in conventional randomized controlled clinical trials
A year or two post-launch, real-world data can provide evidence on the actual effectiveness and economic impact of a new product.
Pharmaceutical and medical device companies will need their health outcomes groups to work more closely with medical affairs to share real-world data as it relates to duration of treatment, disease burden, validated surrogate endpoints, clinical outcome assessments, and quite a number of other health outcome measures. Collaboration between the two groups will be essential.
CHALLENGES: Careful analysis and interpretation of real-world data is critical due to the lack of randomization during clinical trials - the safety picture may change !
The critical key is not the amount of RWD, however, it is the quality of the data. Since this data is collected from a much larger sample size, the challenge of maintaining structure and standardization becomes greater, as the sample size increases. Structured, standardized data is essential, as always, for meaningful analysis of data collected through RWD/ PMS.
 
Registries – data collected and stored at registries – most typically by ICD or CPT code.
CHALLENGES: Require healthcare providers or others upload/ supply them with data – compliance / obtaining data can certainly be an issue
Registries primarily collect data on a diagnoses – minimal/ inadequate detail on relationship between diagnosis and treatments, no improvements, no complications, etc –only frequency of occurrence- no treatment information – similar to challenges seen with  Claims based data.
 
Enterprise Data Warehouses ( EDWs) - central repositories of  data from one or more disparate sources.
CHALLENGES: mass quantities of data, require providers or others to supply the data, This source also has the challenge of no standardization, unstructured data, data contains an extremely large number of unaccounted for variables – all this makes evaluation and analyzation difficult. These are data silos –  a large collection of unstructured data – very difficult to aggregate, analyze and prove statistical significance.

Please share your experiences with advantages, disadvantages and challenges to using Real world evidence data, post market surveillance, Registry and Enterprise Data Warehouses for products you work with or your company’s products. Your thoughts and experiences on these methods of data collection and evaluation are extremely valuable to your colleagues in the Medical Data Community

Review of a few  of the common data collection and evaluation methods:
Claims/ Billing data – self explanatory - collection of data based on the billing and claims processing system – focus on ICD and CPT codes
Advantages: utilizes a well known structured and standardized data format with well parameters ( ICD, CPT and HCPCS code)
CHALLENGES: Limited by what is paid for. Limited to evaluation only on items included in billing ( ICD,  CPT and HCPCS codes). Many studies have demonstrated that claims data are very limited or inaccurate in assessing clinical outcomes and, increasingly, payers are focusing on clinical outcomes. Claims data loses data that may be relative to give deeper information on the factors and progression of a diagnosis.  Frequently treatments that are utilized may not have a CPT or HCPCS code- OTC treatments, home therapies and many other treatments utilized – fall through the gaps using this method.
Claims only analyzes diagnoses - no related symptoms.  Frequently, we need more granular details related to the diagnosis to analyze the progression of a pathology. Ie dimensions, presence of drainage, type of necrotic tissue for diabetic ulcers. Similarly, we need more granular details to track efficacy of our treatments.  CPT and/or HCPCS codes only give high level details on the treatment that was used. Unfortunately no granular details are considered that can affect the success of the treatment ie strength of a drug, sizes of screws, plates and other internal fixators and many others – safety and efficacy on these treatment specifics  are not considered using this data collection method.

Longitudinal patient data - repeated observations of the same variables over long periods of time- observing long term progression of pathologies and long term effects of treatments.  These are very good at showing long term effects of disease across sections of society and sharing similar particular demographics.
CHALLENGES: Tracking patients over extended periods of time is challenging – a number of our  patients in the study can be lost to follow up – so long term tracking is difficult.
Time - require a sufficient time frame to demonstrate economic impact. This may be effective for high-mortality and high-cost categories but may not be appropriate for chronic illnesses – anything longer term is more difficult to track long term/ chronic illnesses difficult, if not impossible to monitor
 
Please share your experiences with differences seen in product performance, advantages and side effects between claims billing an longitudinal studies. Your thoughts and experiences on this method of data collection and evaluation are extremely valuable to your colleagues in the Medical Data Community

​Medical Data and outcomes - Our goal is to evaluate and shed light on the various methods of medical data collection and evaluation/ analysis of the data produced by each method. This group will review methods of data collection and discuss both the pros, cons, cost and effectiveness of each methodology. Ultimately our goal is to assist members of the “ Medical data community” ( HEOR, Post market surveillance, Real world data evaluation, physician and medical liasons, pharmaceutical and medical device companies and many other key stakeholders) to best determine the type and methodology of medical data collection and evaluation that best suits their needs and situation.
The field of medical data collection and evaluation for HEOR, Post market surveillance, Real world data evaluation, physician and medical liasons, pharmaceutical and medical device companies and many other key stakeholders is one of the fastest growing markets / industries in our world today.
We are all aware that healthcare costs continue to rise - for the Pharmaceutical and medical device companies- the latest publication from Tufts CSDD, ( Center for Study of Drug Dev) shows the average R&D costs to be bring a new drug from inception, through all phases of clinical trials, to market is around $2.6 billion. R&D costs and risks for some of our new biologics have even higher cost than non-biologics.
So pharma and med devices are under all time high pressures to prove safety and value of their products and that their products are truly best treatments.
A report recently published by PharmaForce International earlier this year ( 2017) revealed that pharma companies increased their field-based HEOR personnel by 53% in 2016 alone. Proving the rapid growth of this industry.
Beginning our evaluation of data collection and evaluation methodologies – let’s 1st examine:
Clinical Trials – scientifically controlled study of the safety and effectiveness of a therapeutic agent (as a drug, vaccine or medical device) using a tightly controlled and predetermined group of consenting human subjects – typically before approval and widespread use of a product.
CHALLENGES: limitations on the data obtainable in clinical trials could potentially result in a limited – or distorted understanding, due to constraints of participating patients selected for the study. ( small groups of patients with specific characteristics are used in the clinical trials )
Data from the clinical trials focus only on patients selected for the trial, questions focused on during the trial and for the time / duration of the trial. The data collected and evaluated does not include large data sets as we get with PMS and RWD.
So many other factors can be exposed, once the product is actually launched onto the widespread market. Positives – we learn there can be a number of “ off label “ uses for products that are extremely beneficial to patients. Negative Example:  COX 2 NSAIDS- side effects were seen after widespread release that were not seen during any of the clinical trials.
Please share your experiences with differences seen in product performance, advantages and side effects between clinical trials and widespread release of the product. Your thoughts and experiences on this method of data collection and evaluation are extremely valuable to your colleagues in the Medical Data Community