The FDA's sentinel initiative-A comprehensive approach to medical product surveillance

A method for cohort selection of cardiovascular disease records from an electronic health record system

INTRODUCTION

An electronic healthcare record (EHR) system, when used by healthcare providers, improves the quality of care for patients and helps to lower costs. Information collected from manual or electronic health records can also be used for purposes not directly related to patient care delivery, in which case it is termed secondary use. EHR systems facilitate the collection of this secondary use data, which can be used for research purposes like observational studies, taking advantage of improvement in the structuring and retrieval of patient information. However, some of the following problems are common when conducting a research using this kind of data: (i) Over time, systems and data storage methods become obsolete; (ii) Data concerns arise since the data is being used in a context removed from its original intention; (iii) There are privacy concerns when sharing data about individual subjects; (iv) The partial availability of standard medical vocabularies and natural language processing tools for non-English language limits information extraction from structured and unstructured data in the EHR systems. A systematic approach is therefore needed to overcome these, where local data processing is performed prior to data sharing.

METHOD

The proposed study describes a local processing method to extract cohorts of patients for observational studies in four steps: (1) data reorganization from an existing local logical schema into a common external schema over which information can be extracted; (2) cleaning of data, generation of the database profile and retrieval of indicators; (3) computation of derived variables from original variables; (4) application of study design parameters to transform longitudinal data into anonymized data sets ready for statistical analysis and sharing. Mapping from the local logical schema into a common external schema must be performed differently for each EHR and is not subject of this work, but step 2, 3 and 4 are common to all EHRs. The external schema accepts parameters that facilitate the extraction of different cohorts for different studies without having to change the extraction algorithms, and ensures that, given an immutable data set, can be done by the idempotent process. Statistical analysis is part of the process to generate the results necessary for inclusion in reports. The generation of indicators to describe the database allows description of its characteristics, highlighting study results. The set extraction/statistical processing is available in a version controlled repository and can be used at any time to reproduce results, allowing the verification of alterations and error corrections. This methodology promotes the development of reproducible studies and allows potential research problems to be tracked upon extraction algorithms and statistical methods RESULTS: This method was applied to an admissions database, SI³, from the InCor-HCFMUSP, a tertiary referral hospital for cardiovascular disease in the city of São Paulo, as a source of secondary data with 1116848 patients records from 1999 up to 2013. The cleaning process resulted in 313894 patients records and 27698 patients in the cohort selection, with the following criteria: study period: 2003-2013, gender: Male, Female, age:≥18years old, at least 2 outpatient encounters, diagnosis of cardiovascular disease (ICD-10 codes: I20-I25, I64-I70 and G45). An R script provided descriptive statistics of the extracted cohort.

CONCLUSION

This method guarantees a reproducible cohort extraction for use of secondary data in observational studies with enough parameterization to support different study designs and can be used on diverse data sources. Moreover it allows observational electronic health record cohort research to be performed in a non-English language with limited international recognized medical vocabulary.

What Can Big Data Offer the Pharmacovigilance of Orphan Drugs?

The pharmacovigilance of drugs for orphan diseases presents problems related to the small patient population. Obtaining high-quality information on individual reports of suspected adverse reactions is of particular importance for the pharmacovigilance of orphan drugs. The possibility of mining "big data" to detect suspected adverse reactions is being explored in pharmacovigilance generally but may have limited application to orphan drugs. Sources of big data such as social media may be infrequently used as communication channels by patients with rare disease or their caregivers or by health care providers; any adverse reactions identified are likely to reflect what is already known about the safety of the drug from the network of support that grows up around these patients. Opportunities related to potential future big data sources are discussed.

Electronic health records to facilitate clinical research

Electronic health records (EHRs) provide opportunities to enhance patient care, embed performance measures in clinical practice, and facilitate clinical research. Concerns have been raised about the increasing recruitment challenges in trials, burdensome and obtrusive data collection, and uncertain generalizability of the results. Leveraging electronic health records to counterbalance these trends is an area of intense interest. The initial applications of electronic health records, as the primary data source is envisioned for observational studies, embedded pragmatic or post-marketing registry-based randomized studies, or comparative effectiveness studies. Advancing this approach to randomized clinical trials, electronic health records may potentially be used to assess study feasibility, to facilitate patient recruitment, and streamline data collection at baseline and follow-up. Ensuring data security and privacy, overcoming the challenges associated with linking diverse systems and maintaining infrastructure for repeat use of high quality data, are some of the challenges associated with using electronic health records in clinical research. Collaboration between academia, industry, regulatory bodies, policy makers, patients, and electronic health record vendors is critical for the greater use of electronic health records in clinical research. This manuscript identifies the key steps required to advance the role of electronic health records in cardiovascular clinical research.

Big Data Transforms Discovery-Utilization Therapeutics Continuum

Enabling omic technologies adopt a holistic view to produce unprecedented insights into the molecular underpinnings of health and disease, in part, by generating massive high-dimensional biological data. Leveraging these systems-level insights as an engine driving the healthcare evolution is maximized through integration with medical, demographic, and environmental datasets from individuals to populations. Big data analytics has accordingly emerged to add value to the technical aspects of storage, transfer, and analysis required for merging vast arrays of omic-, clinical-, and eco-datasets. In turn, this new field at the interface of biology, medicine, and information science is systematically transforming modern therapeutics across discovery, development, regulation, and utilization.