Dan Cidon, Chief Technology Officer, NextGate

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The Government Accountability Office (GAO) has just confirmed for the public what we in the healthcare industry have known and struggled with for years: how hard it is to correctly identify and match patients to their electronic health records. Whether records for different individuals are incorrectly matched or records for the same individual aren’t matched at all, patients and the organizations that care for them remain at risk because of poor EHR interoperability. The GAO, in a 

 published last week, found significant challenges to patient identification when dealing with EHRs.

Inadequate Health Records Are Failing Mothers and Providers

Transcription errors when entering patient information

Incorrect or inconsistent information provided by the patient, including nicknames in place of formal names

Changes in demographic information, like address or phone number

Inconsistencies in IT systems’ collection of data

Inconsistencies in formatting records, either in the EHR or due to an organization’s policies

Some patient records are harder to match, such as those of newborns, who often have temporary names that aren’t updated; cultural differences that affect standardization of names; or transgender inaccuracies

The GAO stressed that these mismatches are not only a barrier to data sharing but can also adversely affect patient safety and privacy.

All seven of the providers interviewed by the GAO used manual medical record matching; six of them also used health IT tools to help them automatically identify and match records stored in their EHR and other data systems. The five health information exchanges (HIEs) interviewed also reported that they used a range of automated and manual approaches to match records.

The GAO suggested several ways to enhance data matching, such as improving the underlying data itself by standardizing it in EHRs, improving data accuracy by using smartphone apps and other tools to reduce manual errors and enable patients to update their data, sharing best practices, using national patient identifiers, public/private collaboration and using biometric data to verify information.

 to the GAO report, Pew Charitable Trusts agreed that there needs to be a multi-pronged approach to resolve the problem.

It also pointed out that two of the strategies highlighted in the GAO report&shy; — data standardization and biometrics &mdash; have previously been backed by Pew. It recommended that the Office of the National Coordinator of Health IT (ONC) take “immediate action” to standardize patient data in EHRs and for the private sector to “begin to lay the groundwork” on incorporating biometrics.

Health IT: The Most Practical Short-Term Solution

Unfortunately, some of the avenues to improve patient matching are not practicable today. For instance, even if ONC took immediate action to standardize these data elements, it would take months, if not years, to finalize the project.

Adoption of a nationwide matching strategy has been part of policy discussions since Congress dismissed the concept of a unique patient identifier system in 1998. While a coordinated approach to patient matching is needed, a universal identifier alone will never be enough to achieve complete integration of data across the continuum. And developing public-private collaboration to improve matching is hampered by lack of resources, according to the GAO report.

However, there are health IT tools that exist today that can step in now to help resolve this problem, which costs the U.S. healthcare system 

An enterprise master patient index (EMPI) is one piece of technology already helping healthcare organizations accurately and automatically match patient data. Tired of EHR systems that only provide a limited view of a patient’s health history, progressive healthcare organizations are leveraging EMPIs as a strategic advantage to integrate multiple data sources more quickly and efficiency.

For many institutions, patient identification tools like EMPIs are rapidly transforming from a line of defense against duplicate medical records to the default approach for interoperability and enterprise-wide connectivity across care settings. Primary growth in EMPI investments are taking place in the cloud, which allows evolving organizations to scale with greater agility and integrate outside sources of information more easily, including social care and third-party data.

There’s a reason that most of the providers and all of the HIEs interviewed by the GAO augment their manual record matching with electronic assistance: It works. In an era of frequent, large-scale mergers and the transition away from fee-for-service, longitudinal medical records and robust interoperability will become competitive market advantages — and the need for EMPI technology as part of multi-pronged approach to patient matching will become progressively in demand for quality interventions and coordinated, patient-centered care.

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Can an enterprise master patient index help healthcare providers?

What Healthcare Can Do to Address GAO's Concerns About Patient Matching

Machine learning and cloud computing are two of the fastest growing technologies in healthcare. Increased focus on accountable care and a growing trove of generated health data have forced many organizations to rethink their health IT infrastructure and embrace new innovations as part of their overall clinical and financial strategy.

Harnessing the power of cloud and machine learning has become a distinct, competitive advantage for data-driven healthcare organizations looking to glean richer insights in a timely and more cost-efficient manner. Employing machine-learning algorithms allows organizations to piece together fragmented, often disconnected sources to gain predictive, actionable data insights across the enterprise.

While advances in cognitive computing are helping organizations map care pathways and processes, reduce costs in care and garner patterns in patient data to treat and diagnose with greater accuracy, the task of implementing machine-learning projects comes with its challenges.

Poor data quality continues to undermine treatment, outcomes and costs. Good analysis won’t result from bad data, but because clinical documents and medical images are far too large for the human mind to compute, machine-learning projects are becoming more frequent, especially as population health and value-based care initiatives become increasingly critical.

Recent examples show that large hospitals, academic medical centers and technology companies are applying machine-learning algorithms to a variety of use cases, including mapping cancerous immune-cell patterns that will help guide new cancer therapies, identifying heart blockages or applying the technology to the data of discharged patients in an effort to identify which patients have the highest risk for readmission.

As the momentum to adopt machine learning ramps up, healthcare organizations will need to design a plan that takes advantage of the insights they gain as they look to customize treatments, improve diagnosis decision making and forecast the spread of infections. These goals will be key drivers of their operations especially against the background of their need to produce quality care metrics that enable them to be paid under value-based care payment programs.

To conduct a successful machine-learning project, many health organizations are turning to third-party managed service providers (MSPs) that already have experience handling large volumes of patient data sets and can provide valuable offerings such as patient matching tools and cybersecurity technology. Engaging an MSP that has a partnership with a reputable, HIPAA-compliant cloud vendor can help the healthcare organization build, train and host its machine-learning models at scale. Additionally, customized solutions with onboard computing power capable of running real-time deep learning inference on sophisticated models helps deliver the performance, efficiency and responsiveness healthcare organizations want to see.

At the heart of machine learning, an area of artificial intelligence, is the ability to perform pattern recognition, probability theory, optimization and statistics. Machine-learning algorithms can be trained to learn from the data, build a model to recognize common patterns, devise data-driven predictions and uncover insights that contribute to informed decisions.

One example of how machine learning can be applied in healthcare is the case of performing demographic matching of data for an enterprise master patient index (EMPI) — a centralized database containing patient medical records across various departments and geographic locations. Patients are assigned a unique identifier in the EMPI, but data that come from multiple sources can have input errors, name variances, duplications and other precarious inaccuracies.

Unlike traditional algorithms, machine-learning algorithms can adjust themselves based on the feedback provided by human intervention. In the case of the EMPI and its primary goal of demographic matching, the training process for machine learning hinges on manual remediation typically performed by health information management (HIM) professionals responsible for reviewing and linking duplicate records together under a single identifier.

READ: How AI Is Shaking Up Healthcare, Beyond Diagnostics

This manual intervention tends to occur in cases where there is ambiguity between two or more records, and the action performed represents an enormous amount of information that traditional algorithms simply discard. The challenge in using this kind of information is in the sheer number of human interactions required for an algorithm of this type to truly outperform human remediation. This is because the system must be able to detect broad patterns where users consistently take an action of marking a pair of records unique or as a match.

Training, however, is greatly simplified in a cloud environment where usage statistics across many implementations can be gathered to produce a highly intelligent record resolution algorithm, thereby reducing manual duplicate resolution tasks and diminishing false-positive/false-negative errors. Data centralization in the cloud is also cost effective because resources can be dynamically allocated to multiple customers on demand.

The Business Advantage of Machine Learning

While healthcare organizations must identify, harvest and normalize data prior to a machine-learning project, they’ll have to keep in mind their efforts will be used toward the greater goal of meeting specific performance metrics under their health insurers’ payment programs. For example, under the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA), there are quality payment programs such as the Advanced Alternative Payment models (APMs) and the Merit-based Incentive Payment System (MIPS). These initiatives require health providers to receive payments based on their performance and improved patient outcomes.

Other trends are affecting health data analytics, too. Population health management programs, which involve treating and monitoring groups of patients with specific medical conditions such as diabetes, hypertension or cancer, are increasingly being implemented.

Additionally, data that incorporate social determinants of health such as biology and genetics, behavior, economic status or social environment (housing, education, transportation, income and food insecurity), as well as other factors, are important health-related data that need to be included when analyzing the health and wellness of an individual, a group or wider population.

As healthcare organizations embark on a machine-learning project, they’ll have to ask themselves the following questions:

What are the best use cases for a machine-learning project, and where can the organization reap the best return on investment from the project?

Does the organization have the right talent, data and technology to execute machine-learning opportunities?

How can the organization build trust and transparency into machine-learning platforms and applications?

As more and more healthcare organizations step out of the traditional boundaries of their legacy infrastructure and embrace cloud and machine learning to overcome the complexity of their disparate, expanding IT environment, the more they can learn from their data, yielding greater quality insights. However, those embarking on machine-learning projects must remember that their success will hinge on highly skilled resources and how well their organization deploys these insights into actionable measures that improve outcomes, reduce costs and raise the quality of care.

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