Lack of ascertainment of unique patients
Although HIPAA initially required the creation of a health identifier in 199622, federal funds for unique universal patient identifiers have been banned since Congress prohibited their use due to privacy concerns23. Our failure to implement national, unique identifiers in the USA linking a patients data to their healthcare professionals and HIS systems leads to unlinked, incomplete, and often duplicated records, and is another significant source of data quality problems that have been avoided incountries that have implementeunique identifiers23. In addition, it is still nearly impossible for a person to access their own vaccination records if they are in databases separate from their EHR records or were submitted by paper or fax. It is also difficult or impossible to carry out the early cancer prevention studies24 that require that complete clinical information be linked to the correct patients even when they change health providers.
Although the prospect of unique patient identifiers raises valid privacy concerns, it can be argued that it would be easier to monitor and protect privacy with a single, properly encoded universal identifier than with a multitude of poorly documented ones. The absence of a unique identifier is actually one of the greatest causes of invasion of privacy, because typically over half of the EHRs in an institution will mistakenly include someone elses data [Personal communication by Dr. W. Ed Hammond] that may be identifiable.
The current reliance on data aggregation techniques to protect patient privacy significantly delays our access to the information and impedes our understanding of the trajectory of diseases in individual patients, with potentially adverse consequences for their medical care and for identifying critical patient-level variables for subsequent research studies. We must therefore invest in better and updated privacy protection systems and law enforcement solutions. As data scientists, we are concerned about the limitations of HIPAA for privacy protection, due to the ease that such data can be re-identified. Our laws and regulations need to balance individual privacy protection, with making data available for improving health outcomes. At a minimum, the approach to governance we adopt must ensure the following: the system is able to identify and control who can have the authorized level of access to the medical records; every user has a unique ID and a secure password; audit trails are used to track every user activity, and to provide accountability; only authorized personnel can access audit trails, and assess who has accessed or modified a record; and the data storage provider is not able to access personal identifiable information.
A single patient identifier also has health equity ramifications in its favor. Patients who are poorer typically have less insurance coverage or none at all and often switch healthcare systems. They are underrepresented in HIS and research studies, and less likely to have their specific needs understood. A unique identifier should improve the representation of these patients in our HIS and thus our ability to address health inequities.
The inadequacy of our current system for data collection is well illustrated by our failure to collect data as fundamental as mortality in a standardized fashion. Fatal outcomes are not incorporated into the medical record unless death occurs during hospitalization. When needed for public health measures, epidemiological studies, and other research, data on death may be obtained from private services that collect information from funeral homes and obituaries, disease registries unconnected to EHRs, or from the National Death Index website. This website is typically late in gathering mortality information as it is collected by a multitude of disparate local and state systems before being reported to the National Center for Health Statistics. Comprehensive data on mortality and cause of death should be methodically linked to clinical data for the over 330 million individuals in the USA (as we have begun to do for COVID-19 cases). This information will allow for the creation of focused decision support systems for clinical data that are better designed to prevent serious and fatal medical errors, one of the top causes of death in hospitals in the USA25.
Clinical laboratories began collecting digitized data in the 1960s. Although these data support 60 to 70 percent of decisions related to diagnosis, treatment, hospital admission, and discharge, they remain poorly codified, complicated to process, and are underused for medical decision-making and research.
USA programs that defined the minimum government standards for EHRs have offered laboratories incentives to adopt proposed standards for messaging and encoding laboratory data. Unfortunately, serious functional problems still exist with the coding of laboratory test identifiers. There are multiple ways for the same analytes to be represented by different labs and instruments and this results in improper assessments of coded terms and incorrect code selection and categorization. Moreover, coding systems often do not allow for transparent incorporation and transmission of the limits of detection of a test, the presence of interfering substances, and how a particular analyte is measured. Also, failure to enforce the use of consistent quantitative units of measure is a frequent source of data errors.
There is a pressing need for an expanded infrastructure to support the collection and distribution of the stable reference standards needed to support the accurate calibration and safe integration of the results from equivalent tests measuring the same analyte,performed by different instrument platforms or laboratories26,27. The Office of the National Coordinator for Health Information Technology recognizes this problem when it states, Harmonization status indicates calibration equivalencies of tests and is required to verify clinical interoperability of results. Tests that are harmonized may be interpreted and trended together, and may use the same calculations, decision support rules, and machine learning models. Tests that are not harmonized should be interpreted and processed individually, not in aggregate with other tests.3
This infrastructure will simplify the identification of a natural functional interoperability pathway that can be used as a backbone for integrating the currently unwieldy, inconsistent, and incomplete data coding standards for laboratory data. An illustration of the consequences of the failure to fully standardize laboratory data collection and calibration of the results is the limited understanding of the evolving prevalence of COVID-19, due to the inability to account for the performance differences of the over 1,000 SARS-CoV-2 diagnostics that are listed worldwide28. We also need to understand their performance characteristics according to the particular purpose for which a test is being performed (e.g., permission to travel, to access specific facilities, etc.) 29.
The world-wide-web and online business transaction systems such as Amazons e-commerce system were built with a clear understanding of the value of interoperability. These systems ensure that the correct data are collected and stored in an organized, automatically aligned format that is optimized to address new communication requirements and analytical functions. Realizing this scenario for health data will require changes in current practices. Since individual enterprises have built one-of-a-kind systems, there are often strong financial reasons not to share proprietary information. Current laws prohibiting information blocking have not accomplished their purpose, because it is impossible to effectively oversee the thousands of unique versions of HIS.
Given this scenario, it would be useful, once the needed information and data routes are identified and categorized, to develop prototype systems to demonstrate the benefits of profound change in how we manage health information. The development, testing, and validation of these prototypes for addressing the various requirements of patient care and research and development should be based on the integrity, completeness, traceability, and usability of the data; on the avoidance of preventable medical errors; and on measurable improvements in health outcomes.
In contrast to other data transactions for which federal regulations are seeking to increase interoperability (e.g., using ICD-10 coding for billing), in the USA there is no clear business model that incentivizes standardization of laboratory data coding and its integration across medical encounters. Nor is there a single coordinated authority in the USA to monitor and enforce the adoption of, and adherence to, such standards or the transmission of intact laboratory data to end users. Interoperable standards for laboratory data are still very immature (paper and fax lab submissions are still commonplace), and still rely on billing codes for managing and understanding this information, despite their limited scope. For example, there are only 12 Current Procedural Terminology codes used for billing reimbursement that identify the COVID-19 or SARS-COV-2 infectious agent or their antibody response30, while the FDA lists 357 identifiers for COVID-19 testing 31.
Therefore, we suggest that one area that we should use as a model for how to achieve interoperability of patient data, and where favorable incentives for reform may already exist, is in the processing of clinical laboratory data in drug marketing applications submitted to the FDA. Currently, such data undergo multiple transformation steps before regulatory submission, and although results in a given new drug application may be calibrated, the results for many equivalent analytes coming from different sponsors, laboratories, and instruments are not necessarily calibrated the same way3,26,27.
We propose to begin the process of prototype development by creating a centralized calibration process for routine and critical analytes so that results collected during clinical trials will be equivalent regardless of the instrument or the laboratory. The aim is to eliminate the severe problems that result from customized data systems and demonstrate that time-consuming mapping and translation errors, and the associated loss of information, can be avoided while adding traceability and clarity to the clinical laboratory data in marketing applications. The recent phenomenon of increased mergers between central labs supporting pharmaceutical company sponsors and labs that support hospital networks will enable the systematic identification and removal of many deficiencies that derived from multiple sources of lab data, and help implementation of robust and universal data standards. We expect that the time and cost savings and the gains in accuracy demonstrated by a prototype system for clinical laboratory data will be welcomed by the pharmaceutical and device industries, the research and public health communities, and patients. In its processing of lab data, this initiative will include all the standardized data elements needed for analysis of regulatory data submissions, including those related to demographics, diagnosis, medical history, laboratory tests, death, and cause of death. Such standards will greatly enhance regulatory review of marketing applications across multiple sponsors and facilitate comparison of clinical trial lab results across applications, providing valuable feedback to the pharma sponsors.
When it reaches a level of maturity, the prototype for handling laboratory and other clinical data in regulatory submissions could be expanded to non-regulatory contexts, including routine patient care. The lessons learned could eventually be applied to the evaluation and certification of EHRs and decision support systems. The knowledge gained in how to create a truly interoperable system could also be used to address the analytical needs of other data resources including registries, repositories of real-world data, and regional data exchanges.
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Recommendations for achieving interoperable and shareable medical data in the USA | Communications Medicine - Nature.com
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