Event Tracking and Tracing with EMR
Can tracking medical events, rather than patients, help us tackle diagnostic error?
Sidney Smith | | Longer Read
Diagnostic errors are a significant type of healthcare-associated harm, reported to affect one in 20 outpatient adults (1,2) and generate US$34 billion in malpractice payments annually in the US (3). A common cause of diagnostic error is failure to respond to medical data in an appropriate manner, often referred to as failing to “close the loop.” With the exception of a few randomized controlled trials, the evidence base for interventions that close the loop for diagnostic tests is limited – and existing studies have not assessed the clinical impact of those interventions (4).
Our current electronic medical record (EMR) system is designed to store medical data linked to a patient’s name and date of birth – just like paper charts before the digital age. Medicine’s continued reliance on this method of data storage reflects its universal bias in assuming that medical information must be tracked at the patient level. It also makes reliably closing the loop much more challenging, raising the risk of diagnostic error (4).
Could our 50 years of EMR development experience and the relatively recent expansion of advanced logistics companies like Amazon, Google, and FedEx challenge our basic assumptions about tracking medical information? Solving the challenge of closed treatment loops requires us to reorient the way we track medical data. Let us first look at how EMRs store and track medical data today.
Successes… and failures
The purpose of the EMR is to document patient care and store medical files. Records are saved in electronic files linked to patients’ unique identifying information, such as their date of birth, social security number, unique medical record number, or address. Attaching the patient’s unique identifying information to medical events, such as a lab or pathology report, enables the EMR software to file information in the patient’s chart.
These EMR files are stored as PDFs using a common EMR software language called Health Language 7 (HL7). All EMRs use HL7 software – but no two file the patient’s medical information in the same way, which means that they cannot easily send files between vendors. This failure is referred to as lack of interoperability.
In the EMR software, each patient has a unique file with subfiles for labs, imaging, pathology results, and physicians’ notes. Today, patients have access to their files through patient portals for every physician and hospital where they have received treatment – thanks to EMR. And that’s not its only benefit; consider quick, shared access to patient records, the automatic return of lab and imaging results, the ability for multiple users to use charts simultaneously, electronic prescribing, integrated physician dispensing, checking of drug interactions and medication allergies, file recovery after disasters, spellchecking, improved legibility…
These benefits are incontrovertible – but, nonetheless, several of EMR’s most important goals are not being achieved: interoperability; collaborative quality care; effective communication; and dynamic, patient-centric medical records. Why has our current EMR software failed to meet these goals – and what steps can we take to achieve them?
Aiming for interoperability
A seemingly simple solution to attaining interoperability is the creation of a single large electronic storage system, or health information exchange (HIE) that would provide every patient with a single portal to which every physician and health system would send information.
As a result of the Health Information Technology for Economic and Clinical Health Act of 2009, large HIEs are being created – but resistance from EMR vendors and large healthcare systems has made it challenging. Medical data is a commodity and a competitive advantage for EMR companies. Easily sharing medical information between EMR vendors is not in their financial interest. Many vendors have been accused of “information-blocking,” or intentionally interfering with the flow of information between systems (5,6). Health systems may also coerce providers to use specific EMR vendors rather than making it possible to collaborate across different vendors. In addition, hospitals and health systems share patient health information either selectively or inconsistently. Their apparent motivation is improving their revenue and enhancing their market dominance by controlling patient referrals and having exclusive access to patient data (6).
It is likely that the risk of fines imposed by the Office of the National Coordinator for Health Information Technology will reduce this resistance and HIEs will eventually become a reality. Unfortunately, just like the HL7 EMR software, HIEs track medical data at the patient level and will therefore fail to achieve EMR’s ultimate goals. So if neither EMR software vendors nor HIEs can achieve those goals, what can?
A borrowed solution
To meet medicine’s full potential in terms of patient safety, quality, and efficiency, we need to track medical data differently. Rather than tracking a patient with a medical event, such as a biopsy or imaging report, we should track medical events and link them to the patient, a process referred to as medical event tracking (MET).
Consider that every transaction-based industry in the country – including shipping companies, airlines, retailers, and banks – assigns each transactional event a unique “confirmation” number to identify, track, and manage all activity related to that event. The same chain-of-custody approach can be employed in tracking medical events. However, rather than tracking a physical object, the confirmation number can link all communication and documentation between care providers, laboratory personnel, and the patient. Alerts, notes, and patient communication can be incorporated into this solution – effectively closing the treatment loop.
Transactional event tracking software infrastructure creates a digital space for the care continuum to interact, sharing information, quality metrics outcomes, and common medical data storage. MET can also enable direct patient engagement. Linking tracking numbers for each patient’s care team interaction creates the first linked care continuum.
Unique to MET is the concept of medical data life cycles (MDLC). Each medical event has a definable lifespan. For example, a benign skin biopsy has a relatively short MDLC and associated event documentation. The associated event data includes tracking the physical location of the specimen to the lab, communicating the report to the physician, and finally notifying the patient of the benign diagnosis.
In contrast, a skin biopsy demonstrating a melanoma has a MDLC that lasts the lifetime of the patient. This event would include the same initial linked data as the benign biopsy, but would also include tracking numbers for special stains, genetic studies, pharmacological treatments, and future skin examinations. The initial tracking number serves as the reference key to which all subsequent linked events are digitally attached.
An additional critical step in the MET process enables a physician to recommend future events, communicate instructions to the care team, and create time metrics to make sure care is delivered in a timely manner. For example, when a diagnosis of melanoma is made, the pathologist links a recommendation of excision by attaching a code to the tracking number. This recommendation code links a series of time metrics for calling the patient, scheduling the excision, and excising the melanoma. The entire care team, including the pathologist, physician, and patient, is notified if the appropriate steps are not taken in a specific timeframe. The ability for an individual physician to link future events with quality controls in this way did not exist in medicine before MET. Using MET with pathology reports means that no specimen is lost, every pathology report is received by the physician, every patient is notified, every cancer is treated, and future care is coordinated.
Another significant advance with MET is the creation of a “living PDF file” that eliminates “chart flipping” or the need to move from a pathology report to another section of the chart to determine whether or not a patient received treatment. Through embedded tracking numbers in PDF pathology reports, future linked medical events are retrospectively added to linked PDF files. By hovering over the pathology report, care providers can see the full sequence of events linked to the report. This information is “sent back in time” to prior reports so that any pathology report describes all subsequent related future events.
The first commercialized MET platform was created in 2013. The technical advance enabling its development was the insertion of software between the EMR and the lab information software (LIS) located in the application program interface (API). Using this bridge between the EMR and LIS, the MET software creates a unique tracking number shared by the practice, pathologist, patient, courier, medical malpractice company, and insurance company. Using the EMR’s computerized physician order entry system for ordering a biopsy, the tracking platform creates a unique tracking number and a radiofrequency identification device (RFID) label for the specimen bottle. The patient (via app), the physician, and the pathologist are simultaneously linked to the entire data life cycle of the event. Every stakeholder tracks the physical location of the specimen from the office to the lab with all parties receiving real-time notifications about all specimen location transitions.
Today, MET is used to coordinate cancer care – but it will soon be used to coordinate the entire care team interaction, integrate genetic testing and pharmaceutical therapy, track patient outcomes, integrate patient mobile devices, and enable expanded research.
Moving to MET
Adopting integrated MET across the care continuum addresses interoperability issues, creates shared quality metrics, addresses communication deficiencies, and creates a dynamic, patient-centric medical record.
Creating a shared taxonomy for assessing data quality addresses the five dimensions of EMR data quality: completeness, correctness, concordance, currently, and plausibility (7). These features allow high-quality data to be stored and presented in a usable manner, providing reliable, accurate, and actionable information. Uniquely, this approach eliminates the highly variable correctness and completeness results observed with current HL7 EMR software.
The MET system standardizes the quality metric database, eliminates inconsistency across data elements, provides real-time information and communication, allows data segmentation, tracks completed tasks, stores information prospectively, integrates data retrospectively through embedded PDF tracking numbers, and unifies the data storage between care partners. The system generates clinical quality measures through defined data life cycle communication and performance metrics of the care team, thus documenting care transitions and outcomes. Additionally, MET allows practices and communities to accurately measure performance, identify care delivery and workflow issues, make needed corrections – and even enable efficient transition to value-based payments.
With open MET technology, users and developers can create customized templates that integrate into their clinical workflows and maximize data completeness, creating an efficient structured data entry system (8). They can also adjust templates to physician preference based on encounter-specific variables, such as diagnosis, complaint, or other findings, to create structured data narratives.
Because MET provides unique API software insertions between systems, costly EMR upgrades are unnecessary; there is no additional cost for extraction software or services, system reconfiguration, or developing or purchasing reporting and analytics software. MET adoption has little impact on physician and staff workflow, thus minimizing the time and expense of staff training. In addition, the data quality review and resolution process takes up little staff time.
With the creation of high-quality, real-time data, MET enables primary and secondary uses of data and supports the development of a learning healthcare system. Real-time data can drive quality improvement, performance reporting and benchmarking, and clinical decision support; create a patient engagement digital space; foster payment reform and pay-for-performance; support health services research; and develop the next generation of patient-centric medical records that move beyond HIEs.
In short, event-based medical tracking adopts the most advanced communication platforms, used by the most successful communication industries throughout the world, for use in healthcare. MET enables medicine to achieve the goals of interoperability, shared quality metrics, better communication, and creation of a new dynamic, patient-centric medical record. It integrates efficiently, effectively, and economically into existing EMR vendor systems to impact the entire care continuum. Most importantly, MET allows practices and communities to accurately measure performance, identify care delivery and workflow issues, make needed corrections to deliver the highest quality, evidence-based care, and enables the movement to value-based care.
- Institute of Medicine, “Crossing the Quality Chasm: A New Health System for the 21st Century” (2001). Available at: https://bit.ly/3w7TY3l.
- H Singh et al., “The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations,” BMJ Qual Saf, 23, 727 (2014). PMID:
- AS Tehrani et al., “20 year summary of US malpractice claims for diagnostic errors from 1985-2005.” Presented at the 33rd Annual Meeting of the Society for Medical Decision Making; October 25, 2011; Chicago, Illinois, USA.
- ECRI Institute, “Closing the Loop on Diagnostic Tests: Information Technology Solutions” (2017). Available at: https://bit.ly/3y8kZ8d.
- Office of the National Coordinator for Health Information Technology, “Connecting health and care for the nation: a shared nationwide interoperability roadmap” (2015). Available at: https://bit.ly/363mZ5W.
- J Adler-Milstein, E Pfeifer, “Information blocking: is it occurring and what policy strategies can address it?” Milbank Q, 95, 117 (2017). PMID: 28266065.
- NG Weiskopf, C Weng, “Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research,” J Am Med Inform Assoc, 20, 144 (2012). PMID: 22733976.
- ST Rosenbloom et al., “Interface terminologies: facilitating direct entry of clinical data into electronic health record systems,” J Am Med Inform Assoc, 13, 277 (2006). PMID: 16501181.