Diagnostic errors are a significant type of health care-associated harm,1 reported to affect 1 in 20 outpatient adults.2 These errors are responsible for $34 billion in malpractice payments annually in the U.S.3 A common cause of diagnostic error is failure to respond to medical data in an appropriate manner, often referred as to failing to “close the loop.” With the exception of a few randomized control trials, the evidence base for interventions to close the loop for diagnostic tests is limited; additionally, these studies do not assess the clinical impact of the 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, as was the case with paper charts before the digital age. The continued reliance on this method of data storage reflects medicine’s universal bias of 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 and offer a new solution? 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.
The Current EMR Data Tracking System
The purpose of the EMR is to document patient care and store patient 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 into the patient’s chart. This type of storage is identical to the method used in paper charts prior to the creation of EMR.
The technical name for these EMR files is portable document format (PDF) file. The PDF files are stored using a common EMR software language called Health Language 7 (HL7). All EMRs use HL7 software. However, none of the EMR vendors file the patient’s medical information in the same way, which means that EMRs 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 sub-files for labs, imaging, and pathology results, and for physicians’ notes. Today, patients have access to their files through patient portals for every physician and hospital where they have received treatment. Enabling patients’ access to their medical records via patient portals is an advancement created by EMR.
Benefits that have been achieved through creation of the EMR HL7 PDF storage software include shared and quick access to a patient’s records and the automatic return of lab and imaging results. Other benefits include the ability for multiple users to use charts simultaneously, electronic prescribing, integrated physician dispensing, checking of drug-drug interactions and medication allergies, recovery of files after disasters, spell checking, and improved legibility.
While these benefits are not questioned, several of the most important goals that prompted the development of EMR 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? What is the next advancement needed?
Understanding the Failure to Provide Interoperability
On the surface, the solution to attaining interoperability would be creating a single large electronic storage system, or health information exchange (HIE). Use of HIE would provide every patient with a single portal to which every physician and health system would send patient information.
As a result of the Health Information Technology for Economic and Clinical Health Act of 2009, large HIEs are being created. Creating these HIEs has been a challenge due to resistance from EMR vendors and large heath care systems. Medical data is a commodity and competitive advantage for EMR companies. Simply sharing medical information between EMR vendors is not in their financial interest. Many EMR vendors have been accused of “information blocking” or intentionally interfering with the flow of information between systems.5-6 Also, health systems routinely coerce providers to adopt and use certain EMR vendors rather than simply making it possible to collaborate across these technologies.5-6
In addition, hospitals and health systems either share patient health information selectively or fail to consistently share complete information.6 The apparent motivation of these health systems 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 (ONC) will reduce resistance from EMR vendors and health systems, and HIEs will become eventually a reality. Unfortunately, just like the HL7 EMR software, HIEs track medical data at the patient level, and will therefore fail to achieve the ultimate goals of EMR.
If every commercialized EMR software vendor and all future HIEs are unable to achieve our desired goals, what technology will?
Event Tracking: Adopting an Innovation from Other Industries
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 can adopt an EMR communication system that tracks medical events and links the event to the patient, a process referred to as medical event tracking (MET).
Consider that every transaction-based industry in the country – including shipping companies like FedEx and UPS, airlines like Delta and United, retailers like Amazon and Walmart, and banks like Wells Fargo and BB & T– 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 the tracking of medical events such as biopsy specimens, clinical pathology reports, and radiology reports. However, rather than simply 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 to effectively close the treatment loop.
Utilizing a transactional event tracking software infrastructure similar to the ones used in the logistics industry, MET assigns a tracking number for each medical event, which 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 life span. 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, communication of the report to the physician, and finally notification of 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 data 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.
Following the MET Process
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 finally 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 of an individual physician to link future events with quality controls in this way has not existed 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,” which eliminates
“chart flipping” or the need to move from a pathology report to another section of the chart to determine if a patient received treatment. Through embedded tracking numbers in PDF pathology reports, future linked medical events are retrospectively added to linked PDF files. By simply 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, PathologyTracker.com, 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 software 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. Utilizing the EMR computerized physician order entry (CPOE) system for ordering a biopsy, the tracking platform creates the unique tracking number and a radiofrequency identification device label (RFID), which is affixed to the specimen bottle. The patient (using an application), 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 care for an identified cancer but will soon be used to coordinate the entire care team interaction, integrate genetic testing, integrate pharmaceutical therapy, track patient outcomes, integrate patient mobile devices, and enable expanded research.
Benefits of Adopting MET
Adoption of integrated MET across the care continuum addresses care interoperability issues, creates shared quality metrics, addresses communication deficiencies, and creates a dynamic patient-centric medical record. Utilizing MET, all data, recommendations, and quality metrics pass through the patient’s platform, which creates a dynamic patient-centric medical record. Because MET allows any EMR platforms to integrate and enables shared, harmonizing data configurations, it provides passive data integration that creates the continuity of care 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 manner that is usable, providing reliable, accurate, and actionable information. Uniquely, this approach eliminates the highly variable correctness and completeness results observed with current HL7 EMR software.7
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 the care partners. The system generates clinical quality measures though 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, and make needed corrections to deliver the highest quality, evidence-based care. It also allows for 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 (SDES).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, little staff time is required to perform the data quality review and resolution process.
With the creation of high-quality real-time data, MET data enables the primary and secondary uses of data and supports the development of a learning health care system. Real-time data can be used to drive quality improvement, performance reporting and benchmarking, and clinical decision support; create the 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.
Medicine’s historical bias to track medical data at the patient level has impeded our ability to achieve the goals of EMR interoperability, collaborative quality care, effective communication, and dynamic patient-centric medical records. Event-based medical tracking adopts the most advanced communication platforms, which are utilized by the most successful communication industries throughout the world, for use in health care.
MET enables medicine to achieve the goals of interoperability, shared quality metrics, better communication, and creation of a new dynamic patient-centric medical record. MET integrates efficiently, effectively, and economically into existing EMR vendor systems to impact the entire care continuum. 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. Committee on Quality of Health Care in America, Institute of Medicine. Washington, DC, USA:National Academies Press; 2001.
- Singh H, Meyer AN, Tomas EJ. The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ Qual Saf 2014;23(9):727-31.
- Tehrani AS, Lee H, Mathews S, Shore A, Frick K, Makary M. 20 year summary of US malpractice claims for diagnostic errors from 1985-2005. In: 33rd Annual Meeting of the Society for Medical Decision Making, Chicago, Il, pp. 22-26. Qual Saf Health Care 12015;9(5):e5.
- Closing the Loop on diagnostic Test: Information Technology Solutions: Health Technology Assessment Information Services Special Report. ECRI Institute. September 2017.
- Office of the National Coordinator for Health Information Technology. Connecting health and care for the nation: the 2015 nationwide interoperability roadmap. Available at: www.healthit.gov/sites/default/files/nationwide-interoperability-roadmap-draft-version- 1.0.pdf. Accessed March 26, 2020.
- Adler-Milstein J, Pfeifer E. Information blocking: is it occurring and what policy strategies can address it? Milbank Q. 2017;95(1):117-135.
- Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2012;20(1), 144-151.
- Rosenbloom ST, Miller RA, Johnson KB, Elkin PL, Brown SH. Interface terminologies: facilitating direct entry of clinical data into electronic health record systems. J Am Med Inform Assoc. 2006; 13(3);277-288.
MET – Medical Event Tracking
Medical Data Life Cycle
Integrated Recommendation codes
PDF – Portable Data File
Living PDF File
HL7 – Health Language Seven
Dr. Sidney Smith (AOA, The Medical College of Georgia, 1984) is a practicing dermatologist and CEO of PathologyTracker.com.