The American healthcare system has long been burdened by interoperability issues preventing easy access to and sharing of important patient health data. Amid the ongoing COVID-19 pandemic, those issues have created additional challenges for physicians, administrators, and other industry partners. If these problems persist, they could impact provider business models negatively.
Increased consolidation among physician groups during the pandemic has resulted in a corresponding increase in coding operations for many practices. Given the gap between the demand for coders and the trained talent available to meet that demand, organizations have increasingly shifted toward outsourcing to fill critical technical roles. The process of outsourcing these skills, combined with a surge in the number of labor hours needed to meet organizational objectives, could increase the time to code or decrease the quality of output, ultimately creating revenue cycle issues.
If not careful, staffing issues can cause fluctuations in data quality. With less personnel available to ensure the correct information is entered into the correct fields, some organizations have found it difficult to fully harness the power of healthcare solutions to streamline revenue management and operations. Moreover, understaffed facilities may struggle to make the changes to technologies and internal processes that would equip them to take advantage of government programs providing reimbursements for COVID patient care. New CPT codes for COVID have also required insurance companies to update their processes during the pandemic, adding complexities for providers in how quickly they can exchange medical information.
From a clinical perspective, the pandemic’s far-reaching impact on the healthcare system has manifested in the form of lost productivity, resource deprivation, HIPAA breaches, and other, often severe, consequences. However, the strain it has put on payers and revenue cycle management systems has been somewhat less visible from the public eye. In the concerted effort to support clinicians and mitigate the pandemic’s effects on frontline workers, the focus for getting the right data into the right hands to ensure services could be paid for in a timely manner was temporarily deprioritized. Unless these interoperability challenges are addressed as an industry, the cost of healthcare will continue to rise, as will the clunky experiences for both providers and patients.
By Lisa Esch, chief of strategy, innovation and provider industry solutions, NTT DATA Services.
The current state of our healthcare system is in disarray. Healthcare organizations are overworked and understaffed as they deal with the ongoing pandemic resulting in half of all healthcare workers reporting they’ve experienced burnout during this time.
Technology has the potential to solve these challenges, and as more digital health options become available, healthcare practitioners are using more tools that allow them to work more quickly and better serve patients.
Unfortunately, most health technology is developed in a vacuum creating silos of critical information inaccessible and unconnected in caring for patients. Disjointed and disconnected services result in key pieces of information not being available at the time and place required, and productivity can be impacted when healthcare practitioners have to navigate multiple source systems to retrieve data. In turn, this impacts the number of patients that can be seen in a given period and can potentially put human lives on the line if medically critical data is inaccessible in an emergency.
The healthcare community is beginning to embrace a solution to this problem: interoperability. Let’s explore what this means for healthcare providers and why it’s so important in the disorganized, digital healthcare system of 2022.
What’s healthcare interoperability? Why does it matter?
Interoperability services and tools bridge the gap between incompatible systems and data sets, providing a more seamless experience for both patient and provider. It has two primary definitions:
As COVID-19 reshaped American healthcare, interoperability showed real progress with care providers using shared health intelligence more than ever to make care better, safer and more cost-effective, according to the Surescripts 2021 National Progress Report. The report shows how the Surescripts network helped inform billions of healthcare decisions—making prescriptions more affordable, boosting medication adherence, simplifying the specialty medication experience, and fortifying care management processes.
“This year’s National Progress Report demonstrates nationwide momentum toward interoperable, digital health intelligence sharing,” explained Tom Skelton, chief executive officer of Surescripts. “By leveraging the Surescripts network, healthcare professionals of all kinds are getting clinical intelligence at the right time, in the right place, so that they have the trusted insights they need to serve patients.”
The healthcare industry is under intense pressure to improve its efficiency. However, interoperability between technology and various integrated systems presents many challenges that are hindering health facilities from being fully connected and productive.
We have known for years that healthcare needs solutions that artificial intelligence can provide. But the initial proofs of concept have taken too long to materialize. Without clear boundaries and use cases showing how AI in healthcare can work, leadership teams are unable to horizontally collaborate with each other.
How AI in Healthcare Could Solve Interoperability Problems
Technology has the potential to transform the way healthcare works for patients, but right now, interoperability is difficult to attain. Despite industry guides such as the Fast Healthcare Interoperability Resources, data is still a messy business. Data is stored in different ways and in different silos — and not every facility has the ability to read and understand the information contained within the respective silos and make it actionable.
This has a heavy impact on how practitioners work with technology. A radiologist reading film and a doctor making a diagnosis for a chronic pain patient only have access to their siloed expertise. With AI solutions in healthcare, data can be drawn from different disciplines and diagnosis can become faster and smarter.
When used in conjunction with AI, blockchain technology has the power to help practitioners and organizations work together without security risks. Because the blockchain represents a transparent, single source of information that cannot be changed, it can store data from multiple sources and create a harmonized picture of truth that different users can access without bias. In addition, limits can be put in place as to who has access to the data.
This helps healthcare experts form a central hub where the very best knowledge, therapies, and drug research can be pooled, therefore helping target diseases more effectively while keeping patient and research data absolutely secure and private.
It’s clear that leaders at healthcare organizations need to remove the siloed approach and develop an atmosphere of increased collaboration. But how, exactly?
How Blockchain, AI, and Healthcare Can Work Together
Blockchain technology in healthcare helps fulfill all four kinds of interoperability defined by the Healthcare Information and Management Systems Society: foundational, structural, semantic, and organizational. Blockchain’s uses in healthcare create a basis — a structure — where data can live safely and transparently. Then, blockchain can enable a rendering that helps different kinds of readers see and understand the data.
Two aspects of blockchain technology that are especially interesting to the healthcare industry are permissioned blockchains and smart contracts. A permissioned blockchain maintains the privacy of data, knows all the stakeholders, and makes data viewable by actors on the network who are authorized to see it. Smart contracts are “instructions” on the blockchain that are executed automatically once all necessary conditions or events are met. This means decisions can be made available automatically without human intervention. That’s where the power of AI’s uses in healthcare really materialize. This harmonized dataset — coupled with safe and secure automation — means that AI can be used to make faster, better, and more predictive decisions.
Data is the engine behind AI, but it’s also becoming the engine behind healthcare systems and how doctors diagnose and treat patients. If we can aggregate and translate vast amounts of data into streamlined workflows, AI can be used to efficiently diagnose and monitor patients, detect illness, accelerate drug development, and seamlessly run clinical trials.
The ingredients for interoperability are all there, but it’s now up to operators and developers to find ways to work together. The benefits of AI in healthcare are massively transformative — as long as we can find ways to solve problematic perceptions of blockchain and data privacy and get human beings to open up their silos.
No one technology will save the future of healthcare interoperability. It will take collaboration between developers, operators, academics, drug researchers, and an interwoven stack of technologies to bring together a universe of data and put it to good use.
The Cures Act Final Rule’s technical requirements call for radical changes in electronic Patient Health Information Exchange (ePHI). Care providers must adhere to the CoP requirements for patient event notifications (ADT Notifications) and the real-time exchange of ePHI through APIs in 2021. In addition, payer organizations must facilitate the electronic exchange of ePHI between other payers and healthcare providers through a patient access API. They must also provide patients with a list of care providers to choose from for medical services by compiling the provider directory API.
These technical requirements are driven by the CMS’s pursuit of seamless semantic interoperability of healthcare systems and the ONC’s specifications for 2015 requirements of Certified Electronic Health Record Technology. While they affect care providers and payers, health IT developers (HIT vendors) are the catalyst to facilitate the patient centric care.
HIT vendors must swing into action to adhere to their regulatory requirements and enable providers and payers to do so in the process. The stifling competition that is already upon them only lifts the normal for innovation and reflex time. HIT software development requires specialized skill sets and exhaustive processes that escalate costs. In a bid to rein in these costs and adhere to regulatory requirements, HIT developers tend to dilute their competitive edge.
While interoperability has always been one of healthcare’s greatest pain points, the last year or so has emphasized these challenges with the rising demand for data integration and information sharing. The pandemic has required high volumes of data integration, and it’s been difficult for organizations to adapt and respond in an effective and efficient way.
These challenges were further compounded this year with the impending ONC/CMS information blocking rules. With the previous administration’s focus on improving interoperability coinciding with a global health emergency, healthcare organizations had more on their plate than ever. As we look to the future of healthcare in a post-COVID environment, and to the new administration and its healthcare goals, what can healthcare organizations expect?
Healthcare organizations must remain flexible and optimize the organization to be as adaptable as possible. In our interview with Ivan, we explore what healthcare organizations should know about the information blocking rules and the new administration, what is really at the root of the healthcare interoperability problem, and best practices healthcare leaders can employ to set their organizations up for success now and in the future.
How would you define the healthcare interoperability problem?
Interoperability is an evergreen problem across the healthcare industry. As we continue to innovate new capabilities and concepts, we are also constantly expanding our interoperability needs. In a way, interoperability isn’t a problem to be solved. It’s an ongoing practice that has to evolve alongside our other capabilities. For example, there was a time not long ago when social determinants of health (SDoH) were not on anyone’s radar, but as SDoH became more important to healthcare practitioners, it was clear we needed not only to track and store SDoH-related data but also exchange that data across different software systems and organizations. The goal of HL7’s Gravity Project is to build out the standards for exchanging SDOH data using FHIR.
2020 was a tough year in healthcare. The demand for data integration was up, exposing the dire need for better data integration across the healthcare ecosystem. In a world where interoperability wasn’t an issue, how could the pandemic have looked different?
The bad news is that we live in a world where the most reliable COVID vaccination records are stored on paper cards and interoperability is achieved by the patient themselves carrying the card from place to place. In an ideal world, the vaccination would come with an electronic record that the patient could capture on their mobile device and upload to their doctor’s EHR system, their employer’s HR system, and any other third party that needed to see proof of vaccination.
Although we’ve fallen far short of the ideal state, there are some interoperability bright spots to be happy about. For example, we’ve been able to onboard many new sources of lab result data and integrate that into public health departments. This has not always been easy, but because of the ONC’s prior work on the Promoting Interoperability program, we already had agreed-upon standards and an infrastructure in place to move the data from location to location.
Over the course of my career, working in a variety of industries, I have developed certain design patterns when modeling data that guide my approach to tackling a new data domain. One simple example is how I choose the right data type for a given value an application will capture.
While it may sound straightforward, interesting nuances can quickly surface during the data modeling step that necessitate a shared language and vocabulary between the functional experts and the software engineers. In other words, we need to figure out how to work together and speak the same language in order to solve the problem well.
The importance of nuanced semantics may be illustrated with the example of how an anesthesiologist documents the administration of an antibiotic. . The type and timing of antibiotic administration is a key metric that anesthesia providers have historically had to report to Centers for Medicaid and Medicare Services (CMS) since it correlates with both patient outcomes and healthcare costs.
As I analyzed the paper anesthesia record used, I noticed an “antibiotics” checkbox, accompanied by an antibiotic name, an amount, a unit of measure, and the route of administration. These all made sense to me, and I proceeded to incorporate these concepts into my data model. For the antibiotics checkbox, I naively interpreted it as a simple boolean value, and I named it Antibiotics Administered Indicator. In my mind, that simply indicated that the antibiotic denoted on the form was either administered (true), or not administered (false).
During a review of the model, I learned that a clinician interprets this checkbox to mean an “indication for antibiotics”; in other words, antibiotics were or were not determined as a necessary course of action given other clinical conditions. A true value didn’t mean that antibiotics were administered, only that they were indicated, and thus needed to be given. That is obviously a completely different understanding than the one at which I had arrived. Needless to say, this was eye opening for me, even having been down the road of developing a functional understanding of data domains many times before.
The illustration highlights the importance of having both functional (i.e., the doctors) perspectives and technical perspectives present and engaged during software design. A purely technical survey of a subject area will certainly be valuable, and in some cases may provide decent coverage in terms of establishing a foundational understanding of that domain. In most cases, however, a functional perspective will also be required to complete the picture and add the necessary insight required to create an accurate and intuitive user experience.
In fact, healthcare may serve as the poster child for just how challenging, complex, and unforgiving software design can be. Clinician dissatisfaction and fatigue with existing electronic health report software is well documented, and the explanations are plentiful: failed interoperability, difficult user experience, inefficiency with simple tasks, onerous data capture burden, etc. Perhaps the common denominator is a failed understanding of complex and poorly defined clinical workflows being interpreted and standardized in software by technical experts working in isolation. The real issue here is that foundational errors propagate as the software evolves, and there is no easy way to reverse course once construction begins.
By Jerry Rankin, strategy director of healthcare interoperability, Infor.
The unrelenting if unpredictable movement of continental plates builds new mountain ranges and reshapes continents, but for the most part, we do not notice their progress. Such a shift has come to healthcare.
This spring two US Federal agencies, ONC and CMS, announced complimentary Final Rules, signaling tectonic movement in healthcare interoperability. These rules are very consequential for the industry, but while no one can claim that they went unnoticed, the industry has been understandably instead fixated on responding to the COVID-19 pandemic. In response, the federal agencies involved have pushed the implementation timelines back by roughly six months.
The Final Rules
On May 1, 2020, the ONC published a Final Rule implementing provisions of the 2016 21st Century Cures Act. Known in the industry as the “Information Blocking and Health IT Certification” Final Rule, the Provider and EHR focused rule requires developers of Certified Health Information Technology (e.g. EHRs) to make standard APIs available for the delivery of individual and population records, as well as defines the data set and transaction standards of the APIs to be United States Core Data Set for interoperability (USCDI) and FHIR, respectively.
In a parallel action, the CMS issued a ruling implementing provisions of the Cures Act, known as the “Interoperability and Patient Access Rule,” leveraging “Conditions of Participation” in Federal Health programs. The finalized rule requires payers to provide a Patient Access API which gives patients access to certain health data including personal data.
These rules represent an important federal nudge to the industry to move in the next few years to implement and adopt standard, digital friendly APIs for the exchange of key patient information, eliminate policies and practices of health IT vendors, providers and other data holders that constrain the free flow of healthcare data, and, importantly, bring payers and consumers into the interoperability discussion, enabling data to flow across the healthcare ecosystem.
These rules are just the tip of the iceberg, though. The industry has been hard at work for years developing the FHIR API standard, and there are abundant examples of voluntary industry led collaborations working to improve and streamline healthcare leveraging FHIR. For example, the HL7 Da Vinci Project sponsors collaboration among payers, providers and HIT vendors working to define standards-based implementations to improve some of the more costly workflows in the industry. In addition to adoption by traditional HIT vendors, even IT “gorillas” are adopting FHIR. We recently saw this with the launch of Microsoft Cloud for Healthcare which extensively leverages FHIR APIs and data standards.
What to expect for payers and providers?
Payers and providers and their health IT vendors have a great deal of work to do on a tight timeline to meet the requirements put in place and to simply keep up with the pace of change in the industry. Given the massive investment in and footprint of legacy systems, there is a vast amount of work to do to connect systems and data to the emerging FHIR API ecosystem.
In a public health report by the Centers for Disease Control (CDC), the state of the U.S. public health technology was likened to “puttering along the data superhighway in our Model T Ford.”
While the healthcare industry has talked about improving data interoperability with the noble goal of breaking down data silos to better coordinate care and turn data into information, the business of healthcare resisted meaningful change. The status quo that traps data in its silos helped to serve the interests of big, incumbent vendors by locking provider customers into their proprietary tech stacks. In turn, some providers believed they too could protect against patient leakage by holding medical data captive.
Data interoperability is stuck in the past
Even though patients have had, since 1996, a right to access their own information under HIPAA, the healthcare system made it really, really hard to obtain that data. Life Image recently conducted a survey of 1,300 patients and found that 40% of patients had to go to their provider’s office in person to submit requests for medical records. Additionally, 40% received those medical records on a CD, a 1980s technology that is obsolete in the modern consumer world.
In all other industries except healthcare, data requests, collection, storage and exchange are commonplace, and available at your fingertips at any hour and any day of the week. While patient satisfaction and convenience seemed to be worthwhile healthcare goals, they weren’t enough to drive significant, wholesale change and conversion from protectionism, managing resources to optimize the physician rather than the patient, or stubbornly persistent operational practices using CDs.
Nothing happens until something happens
The federal government recognized this inertia and promulgated a lengthy set of interoperability rules in March 2020. Just days later, the force and fury of COVID-19 started hitting the U.S. and created a public health emergency that exposed the significant operational risks and clinical dangers created by the lack of interoperability. Frictionless data sharing was no longer an existential threat. All of a sudden, the hazards became tangible.
The paradox is that COVID-19 has manifested the critical need for exactly what the new federal rules require: advancement of interoperability and digital online access to clinical data and imaging, at scale, for care coordination and infection control. Now more than ever, healthcare needs to be able to digitize, visualize, virtualize, and curate all types of medical data at scale including diagnostic and pathology information without physical exchange. No more CDs, no more faxes, no more film or slides.
Not just data – advanced data
COVID-19 is a respiratory illness with corresponding impacts to the heart, liver, kidney and other organs. People with underlying health conditions such as obesity, diabetes, chronic lung disease and cardiovascular disease appear to be at higher risk for hospitalization and death. Out of the 122,653 U.S. COVID-19 cases reported to CDC, only 5.8% of patients had data available pertaining to underlying health conditions or potential risk factors.
Advanced data such as imaging data are critical to diagnosis, treatment, recovery, and post-care monitoring. The typical structured data found in an electronic health record (EHR) or claims data are easier to access but have limited clinical value. With chronic or complex conditions, advanced data such as medical imaging, pathology and genomics are critical components of the longitudinal patient record that must be easily accessed and shared. However, imaging data has historically been among the most technically challenging to exchange.
While the industry has made some gains in imaging interoperability between large tertiary hospitals and their primary referral sites, patient sharing of digital images online is dismally small.
By Mike Sutten, chief technology officer, and Dr. David Nace, chief medical officer, Innovaccer.
Burdensome documentation and gaps in care have been long-standing challenges in the healthcare industry.
The COVID-19 pandemic has amplified those challenges on a global level, creating a situation in which people have been hesitant to seek care for other medical concerns. As such, healthcare providers are losing revenue, employees are losing their jobs, and those remaining in the workforce are subject to burnout.
In an effort to prevent the spread of COVID-19, many healthcare providers proactively reduced or stopped in-person visits for non-COVID-19 medical needs, ranging from the routine care of a sore throat to treatment of chronic conditions, cancer, and even mental health services.
Additionally, nearly one-third of American adults reported delaying or avoiding medical visits over concerns for possible exposure to the virus, according to an American College of Emergency Physicians and Morning Consult poll. More than half reported worrying about access to their primary care physician or being turned away from the hospital.
As a result, healthcare spending decreased by 18% in the first quarter of 2020, according to the U.S. Bureau of Economic Analysis. Surprisingly, some 1.4 million healthcare workers lost their jobs in April, a sharp increase from the 42,000 reported in March, according to the U.S. Bureau of Labor Statistics.
The global pandemic amplifies the day-to-day challenges of identifying gaps in care, the increased documentation required to track them, and the difficulties associated with determining their effects and responding with appropriate interventions.
The impact of this virus looms over the backdrop of a healthcare environment in which the American Hospital Association (AHA) makes the point is rapidly evolving from a fee-for-service system into a value-based delivery system. As healthcare providers and payers transition to collaborative digital care delivery models, this movement highlights the greater need for a data infrastructure that supports value-based care with sharper and more transparent insights into population health.