Patient Records Abstraction (PRA) is a process done manually by searching through a medical record to identify data required for a particular or secondary use. It consists of direct matching of information found in the record to the data required, but also includes operations on the data such as categorizing, coding, transforming, interpreting, summarizing, and calculating. The abstraction, in the end, summarizes information about a patient for a specific secondary data use. (1) PRA typically involves reviewing patient files and abstracting (i.e., extracting) key data, which are then entered into electronic files. (2) Depending on the measure or purpose, there can be different sources for data collection such as paper medical records, electronic medical records (EMR), patient surveys, administrative databases, etc.

PRA helps in reviewing large or small data sets and documents for information which can be helpful in the future for decision making. (3) It often involves collecting organizationally-defined, clinically relevant data elements, which don’t electronically convert, from the legacy system into the new target system. This process, therefore, makes detailed patient data instantly available in the electronic chart in a faster, accurate and cost-effective manner; (4) facilitating access to care without referring to a paper chart or an EMR. (5)

In order to make informed decisions with the help of PRA, a tool referred to as ‘abstraction hierarchy’ (AH) is often implemented to facilitate cognitive work analysis (CWA). (6) These hierarchies can be used to develop depictions of patient care in line with biomedical knowledge, making medical problem solving easier, and act as a frame of reference. (7)

Studies exploring different aspects of AH also suggest that, it can be useful in implementing shared decision making (SDM) in order to improve patient care through their active engagement. Implementing SDM would be an advantageous approach to care, as patient involvement in decision making can result in improved health outcomes, thus providing an enhanced ethical framework for clinicians to deliver appropriate care and improved efficiency of the health system.7

Researchers have found a way to mine huge amounts of patient data with the widespread use of EMR for identifying the best predictors of health outcomes. Also, every EMR system possibly has only a subset of the information necessary for a particular clinical trial.  This is where PRA can come into play to provide the necessary, overall data, thus substantially saving time and energy. (8) Consistent improvement in healthcare data quality plays a vital role in planning, development, and maintenance of healthcare services. This improvement can affect clinical and administrative decision making in many ways, thereby increasing patient safety, and facilitating the efficacy of clinical care pathways. (9)

In addition to these advancements in healthcare technologies, the concept of ‘Big Data’ is emerging, which seems to have been first derived from an IT strategic consulting group’s approach to manage volume, velocity, and variety of data. (10) Researchers believe that the EMRs that contain huge volumes of patient data in variety of domains could also be considered as ‘Big Data’.  This is owing to the reports stating the United States alone will soon witness one billion patient visits documented per year in EMR systems. (11) Besides, the amount of additional data available about medical conditions, underlying genetics, medications, and treatment approaches is high. (12)

Furthermore, the use of ‘real-world data’ (RWD) that contributes to the ‘real-world evidence’ (RWE) is also on the rise. RWD and associated RWE may constitute valid scientific evidence depending on the characteristics of the data. For making better choices about health and health care requires the best possible evidence. Sadly, many decisions made today lack the high-quality evidence derived from randomized, controlled trials or well-designed observational studies. Therefore, rich, diverse sources of digital data such as, EMRs, claims data, consumer data, chart reviews, which can cumulatively be referred to as ‘Big Data’- are becoming widely available for research, thus facilitating data extraction with the help of robust abstraction tools. To add to this, concept of chart abstraction methodologies that will integrate physician insights is also emerging. This will ensure better understanding of- i) various ways to address common obstacles and limitations to RWD collection, and ii) the importance community physician relationships for study implementation and success. The research and health care communities, consequently, have the opportunity to support improved healthcare decision making. (13)

References

  1. Nahm M. Data accuracy in medical record abstraction. The University of Texas School of Health Information Sciences at Houston.
  2. Half R. What skills do you need to be an electronic medical records abstractor/auditor? July, 2016.
  3. Rasmussen D. How data abstraction is going to change healthcare forever. September, 2016.
  4. Improving patient records: Conclusions and Recommendations. The computer-based patient record: An essential technology for health care. 1997.
  5. Nahm M. Data accuracy in medical record abstraction. The University of Texas School of Health Information Sciences at Houston.
  6. St-Maurice JD, Burns CM. Modeling Patient Treatment With Medical Records: An Abstraction Hierarchy to Understand User Competencies and Needs. Eysenbach G, ed. JMIR Human Factors 2017; 4(3):e16.
  7. Hajdukiewicz JR, Vicente KJ, Doyle DJ, et al. Modeling a medical environment: an ontology for integrated medical informatics design. Int J Med Inform. 2001; 62(1):79–99.
  8. Jones G. EMR to EDC for RWE. May, 2017.
  9. Adeleke IT, Adekanye AO, Onawola KA, et al. Data quality assessment in healthcare: a 365-day chart review of inpatients’ health records at a Nigerian tertiary hospital. Journal of the American Medical Informatics Association : JAMIA. 2012; 19(6):1039-1042.
  10. Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group; 2001.
  11. Hripcsak G, Albers DJ. Next-generation phenotyping of electronic health records. J Am Med Inform Assoc 2013; 20(1):117–21.
  12. Ross MK, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook of Medical Informatics 2014; 9(1):97-104.
  13. Califf RM, et al. Transforming evidence generation to support health and health care decisions. N Engl J Med 2016; 375:2395-2400

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