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Healthcare Interoperability: New Tactics and Technology

The document discusses healthcare interoperability, defining it as the ability of multiple systems to communicate effectively to enhance patient care. It highlights the challenges faced in achieving interoperability, particularly in accountable care organizations and population health, and points to emerging technologies like FHIR and APIs as potential solutions. Ultimately, the aim is to facilitate closed-loop analytics that integrate insights directly into clinical workflows to improve patient outcomes.

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Healthcare Interoperability: New Tactics and Technology ̶ Sean Stohl
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Interoperability: New Tactics and Technology When it comes to defining interoperability, there are many perspectives. Let’s take a high-level look at what some of those are and at the challenges we face in “solving” this puzzle. We’ll also take a look at the Health Catalyst platform, applications and methodologies, and how they are able to achieve some of the requirements of interoperability in a very pragmatic way.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Where a Lack of Interoperability Hits Hardest What does interoperability mean? Simplistically, it’s the ability for multiple systems to talk to each other and integrate data to improve patient care. The Center for Medical Interoperability, with a device-centric approach, defines it as the ability to share information across multiple technologies. What it ultimately boils down to is the idea that all players in the healthcare IT space should play well together.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Where a Lack of Interoperability Hits Hardest Accountable Care – ACOs exist to improve the quality and coordination of healthcare delivered to a defined population. Care managers within an ACO need access to information about the patients for which they are accountable. According to a recent survey of 68 ACOs, around 70% of ACOs struggle to collect data. This struggle is amplified as patients see an increasing number of specialists and out-of- network providers who are unable to convey data back to the initial ACO provider.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Where a Lack of Interoperability Hits Hardest Population Health – Dale Sanders, Health Catalyst Executive VP, describes 12 categories of data required for population health, with the minimal data sets being: 1. Patient reported outcomes 2. Social determinants of health 3. Activity-based costing data These are all but missing from the healthcare data landscape today, gaps that can be attributed to the lack of interoperability.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Where a Lack of Interoperability Hits Hardest Precision Medicine – Precision medicine integrates research with clinical practice to understand a patient’s individual illness and deliver the right treatment at the right time. We’ve been talking about it since 2011, when the National Research Council first issued a news release calling for: …a new data network that integrates emerging research on the molecular makeup of diseases with clinical data on individual patients” for the purposes of developing “a more accurate classification of disease and ultimately [enhancing] diagnosis and treatment.”
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Who Is Impacted and Why? The biggest concern with the lack of inter- operability goes back to the patients who benefit from improved care. Providers can also suffer because they strive to create ideal care. When they don’t have all the information about their patients, who often undergo care elsewhere, this makes it difficult to administer and coordinate overall care. The added costs of trying to get relevant data points out of EMRs ultimately trickles down to the patient.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Who Is Impacted and Why? Inconsistent data quality can easily make two different records for one patient look like two different patients when tracking them from one system to the next. This becomes a master data problem. And from a resourcing and maintenance perspective, there are other costs in managing the different technologies involved with multiple systems, not to mention the churn and waste.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. What’s the Holdup? In the past, EMR vendors haven’t had to worry about interoperability. They’ve had their own kingdoms. But lately, there has been enough public and government pressure to improve patient care, so they’ve started to work with one another and with other large healthcare systems and IT providers. Where EMRs could afford to worry about their own needs in the past, this is no longer the case.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. What’s the Holdup? Some of the biggest struggles with interoperability exist with the workflow and policy differences between organizations and variability in the data captured and different contexts of use. This is where the government has tried to impose standards, but that still takes time and can have unforeseen consequences. One of the top protocol concerns with inter- operability is around standards and getting everyone to agree on the one way to send and receive data.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. What’s the Holdup? HL7 has been the top interoperability standard in the past, and used by most health information exchanges (HIEs) Continuity of Care Documents are another standard. HL7 defines these as fostering: …interoperability of clinical data by allowing physicians to send electronic medical information to other providers without loss of meaning and enabling improvement of patient care.”
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. What’s the Holdup? There have been frustrations when dealing with HL7 data because of its unreliability from both a completeness and consistency standpoint. HIE’s also struggle in this area because of insufficient data standards, inaccurate data, and different privacy rules. With the current variety and volume of technologies there are no easy answers, but some solutions are beginning to separate themselves.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. What’s the Holdup? Application program interfaces (APIs) are being used more and more to interact with Health Systems. An API expresses a software component in terms of its operations, inputs, and outputs, which allows definitions and implementations to vary without compromising the interface. According to The Advisory Board: …the use of a standardized and accessible API is a critical step in allowing the appropriate flow of information across health care stakeholders.”
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. What’s the Holdup? Fast healthcare interoperability resources (FHIR) is an interoperability standard for electronic exchange of healthcare information that is the successor to HL7. FHIR is a healthcare exchange API that provides a simple and efficient way to discover and consume information across distributed systems. FHIR aims to make the implementation of the data exchange simpler so more time can be spent on the non-technical, hard interoperability issues.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. ® HL7 FHIR® ARGONAUT PROJECT What’s the Holdup? Nurturing these ideas falls under the care of The Argonaut Project, which seeks to advance the adoption of interoperability standards: It’s a joint project between HL7 and some of the top healthcare organizations in the U.S., including many top EMRs in the country. EMRs are going to leverage FHIR more and more to both extract data from the EMR and feed it back into the EMR. …“to enable expanded information sharing for electronic health records, documents, and other health information based on the FHIR specification.”
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Ultimate Goal: Closed-Loop Analytics Patients with difficult-to-diagnose conditions (Parkinson’s, heart failure, cancers) will sometimes visit multiple physicians, quite often fragmenting their data ecosystem. The most subjective data content is contained in clinical notes and diagnostic reports, so to solve the subjective problem of diagnoses, we must get our hands on that text data and start bouncing it against our discrete data. We also need 7×24 biometrics, genomic, familial, and socio-economic data to reduce the time to accurate diagnoses.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Ultimate Goal: Closed-Loop Analytics While EMR vendors are slow and improving, the space we want to get to, to truly improve care, is closed-loop analytics. Closed-loop analytics is about closing the loop between analytics and workflow. As a physician works in the EMR, they are creating valuable data on a patient. That data typically is resurfaced in an analytic environment where complex analyses and algorithms can create new data insights that are relevant to clinical care.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Ultimate Goal: Closed-Loop Analytics Traditionally, the analytic insights are delivered in a siloed analytic environment accessed separate from the EMR system. This leads to many valuable analytics never being used to improve patient care because accessing the analytics is cumbersome and not part of the normal clinical workflow.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Ultimate Goal: Closed-Loop Analytics Closed-loop analytics aims to bring the analytic insights directly into the clinical workflow – analytics relevant to patient care should be displayed directly in the EMR. To do this requires getting data from the EMR, feeding it back into the enterprise data warehouse (EDW) to run algorithms and analytics, and then feeding the findings back into the EMR again to improve workflows and care.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. How to Get On Track with Interoperability Using a Late-BindingSM Enterprise Data Warehouse and analytics applications built with this platform, health systems can reduce the pain of getting data out of various systems and into the EDW. This allows the healthcare systems we work with to spend less time on gathering data and more time analyzing it and improving care.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. How to Get On Track with Interoperability One way to improve care is by creating predictive analytics based EDW data, then feeding that data back in the EMR so clinicians can improve care. For example, in our Congestive Heart Failure application, which uses a predictive algorithm to identify patients with the highest readmission risk. Providers use this data to follow up with the highest-risk patients and apply best known practices that help alleviate readmission risk.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. One Approach to Interoperability A best practice approach is to bring multiple source systems into the EDW in a standardized and very efficient way. The Health Catalyst Analytics Platform does this and captures metadata about the source systems. We use that metadata to quickly load the data into the EDW via database direct querying, flat files, or XML files. New connectors are being developed that will provide the ability to insert streaming HL7 messages, claims EDI data, and Hadoop data into the EDW.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. One Approach to Interoperability Regardless of what standards or systems are on the organization’s side, we can get that data into the EDW in a pragmatic way without worrying about getting all the data at once. This approach focuses on initially getting what provides the most analytical value. If it turns out later that there are other metrics needed from a new table, it’s easy to bring that in because of the data acquisition functionality in our platform.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. One Approach to Interoperability We work with one large healthcare system with more than 200 sites of care, including nine hospitals, many coming by way of merger and acquisition. At one point, this was a health system with a single EMR that suddenly found themselves contending with records from Epic, Cerner, Meditech, and some Centricity data. We worked with them to bring all this data into our platform and integrate everything using Source Mart Designer and SAM Designer.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. One Approach to Interoperability ACOs need to integrate various claims systems so their payers have a master data record of the patient to work with. With multiple systems, Health Catalyst can create this common master record for the patient or provider, the single source of truth, from which all analytics can originate. We have begun to ‘close the loop’ with our analytic platform because our applications have been delivered through the EMRs of our healthcare systems and directly into the clinical workflow.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Can We Build a Better Mousetrap? Time will tell if we can achieve true interoperability. We’re a good year or two from seeing the fruits of the FHIR efforts. Once standards are in place, many small, ambulatory clinics running obscure EMRs, without the technical or financial resources to share data will continue to struggle. How do you integrate their data? Some will look to HIEs to help with interoperability, but there are funding issues. Data quality is another big issue; the breadth of data often isn’t enough.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Can We Build a Better Mousetrap? Building a better mousetraps starts with the platform, tools, and approach to get the various source systems data and start analyzing it. Patient providers have Master Data Management tools at their disposal to help in those scenarios. Health Catalyst is developing closed-loop analytics for improving quality and outcomes to get data back to providers for making informed decisions to improve care for their patients.
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. For more information: “This book is a fantastic piece of work” – Robert Lindeman MD, FAAP, Chief Physician Quality Officer
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. More about this topic Link to original article for a more in-depth discussion. Healthcare Interoperability: New Tactics and Technology Clinical Data Management: 3 Improvement Strategies Jane Felmlee, Finance Operations Consultant Going Beyond Genomics in Precision Medicine: What’s Next David Crockett, Ph.D., Research & Predictive Analytics, Sr. Director The Best Way to Optimize Physician Workflow Dr. Ed Corbett, Deputy CMO Healthcare Analytics Applications: Why You Need an Out of Box Solution with Customizability Jason Burke, Technical Director; John Simmons, Senior Data Architect How to Evaluate a Clinical Analytics Vendor: A Checklist Dale Sanders, Executive VP of Software
© 2016 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Sean Stohl started with Health Catalyst in 2012. He oversees the Data Acquisition Services Team in Product Development that works with Health Catalyst’s clients to bring their source system data into their Enterprise Data Warehouse. Sean also works on the Health Catalyst Analytics Platform and enhancing it to bring in new sources of data into the EDW. Prior to joining Health Catalyst, Sean worked at Goldman Sachs in the Private Wealth Management Technology group and Intel Corporation. Sean holds a MS in Information Systems Management and a BS in Business Management from Brigham Young University. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com