Steps to Achieve EHR/EMR Interoperability to Put Patient at the Center of Healthcare

The US healthcare system has been battling quite a few challenges as they continue to track outbreaks, and stay abreast of the latest developments on vaccines and the spread of the disease. But what became glaringly evident during the pandemic was the lack of EHR/EMR interoperability that made sifting through patient information and providing seamless quality care pretty difficult. Although the federal government pumped in billions of dollars to accelerate the adoption of electronic health records, we are still far away from rising to the information challenges clinicians are facing on a day-to-day basis.

A classic case in point – California! It went through public health crises in 2020 as the state with the second-highest number of COVID-19 cases, pinning its hopes on a robust health data exchange. As Claudia Williams, CEO of Manifest MedEx (MX) points out, “Smaller practices don’t know what kind of hospital care the patient received, they don’t know what drugs the patient is on, and they don’t have the tools to conduct that level of risk stratification.”

The Department of Health and Humans Services (HHS) recently published its 2020-2025 Federal Health IT Strategic Plan based on recommendations from more than 25 federal organizations.

Quality of data, user interfaces, and usability concerns, along with the inability of data to adequately support discovery and interoperability among systems – all underline the need to have better EHR/EMR interoperability to put patients at the heart of healthcare.

It’s time we dive deeper into the challenges stakeholders are facing as they proceed towards achieving EHR/EMR interoperability and how we can work towards making it a reality.

EHR and EMR: The fundamental difference

An electronic health record (EHR) is an electronic version of a patient’s medical history that includes test results, present illness and its history, progress notes, immunization, medications, etc. Often confused with an electronic medical record (EMR), an EHR is much broader in scope and offers a comprehensive view of the patient’s health. An EMR also contains medical history along with a treatment plan but it’s often pertaining to one practice and the details will therefore stay with that particular physician or provider and is never really shared when the patient moves on to another physician or provider.

The fact that EHR travels with the patient wherever they go, it gets shared with other physicians and providers helping them make informed decisions. EHR helps maintain continuity of medical care even when patients are moved to a different facility.

But in a complex healthcare environment, EHR integrations are not so easy. EHR solutions used by different medical facilities can differ in features, capabilities, workflows, and infrastructure requirements. Seamless sharing of information will therefore be possible only when we introduce interoperability into the system. This would require stakeholders to tide over the many challenges in attaining healthcare data interoperability.

The top ones include:

  • Absence of a unique patient identifier – Absolutely no or minimum standardization for identifying patients makes data exchange between EMR and EHR extremely tedious.
  • Lack of standardized data – With different standard formats for collating data, the information exchanged varies in format. This poses a barrier to analyzing, storing, and exchanging data seamlessly.
  • Slow FHIR adoption – The use of Fast Healthcare Interoperability Resources (FHIR) is recommended since it describes data formats and APIs for health record exchange and integrates the best of HL7, v2, HL7v3, and CDA while leveraging the best of web service technologies. It provides agility, efficiency, and security to data exchange with perfect standardization of data. The adoption of FHIR application programming interfaces (APIs) has a long way to go before it touches the finish line. While FHIR apps do extract data, they lack the ability to write data back.
  • Data privacy and security issues – Healthcare compliances such as HIPAA can impose limitations on how stakeholders share and exchange data amongst each other and third-party vendors.
  • The relatively high cost of integration – Traditional models can be a tad out of reach of small and mid-sized organizations from a cost perspective.

Interoperability for patient-centric care

Interoperability allows patients to be informed all the time irrespective of which vendor they choose. It ensures:

  • Better patient health outcomes
  • Better quality of care
  • Lower healthcare costs
  • Tailored treatments based on individual history and preferences
  • Greater patient engagement
  • Reduced ambiguity
  • Data devoid of redundancies

Interoperability initiatives should be patient-centric and revolve around improving patient care. The chief objective should be to safely and securely exchange patient information across the healthcare ecosystem where interoperability serves as the linchpin.

As Dr. Farzad Mostashari, the National Coordinator for Health Information Technology at the U.S. Department of Health and Human Services iterates, “(the agency wants to ensure that) information follows the patient regardless of geographic, organizational, or vendor boundaries.”
A CHIME KLAS report suggests 67% (up from 28% in 2017) of providers admitted they often or nearly always had access to the needed patient records in 2020 while only 15% (up from 6% in 2017) believe data exchange has impacted patient care. The Cures Act and many other federal initiatives are now focused on improving patient care through data sharing. Significant progress has been noticed in data sharing across disparate EMRs.

The way to interoperability

There are certain milestones to touch on the road to attaining interoperability. Just like the banking sector where current systems are modified instead of being recreated, the EHR too will benefit from suitably modified systems wrapped in applications and added capabilities.

Here’s what we need to do:

  • Use a population health management system – This will make providers accountable for caring for populations with common health conditions. The system will use data from various sources including EHRs, EMRs, claims, monitoring devices, etc. to give a 360-degree view to providers while helping patients with regular alerts and messages.
  • Leverage the services of Health Information Exchange (HIE) – HIE connects healthcare organizations across the state to allow them to exchange patient data. So if a patient gets admitted into an emergency room, the HIE will access data from other care centers too so as to give an accurate clinical picture of the patient to providers and alert them when a patient checks in to some other facility.
  • Deploy health management apps designed for patients – These are typically expected to help patients aggregate their health data, get health status, track appointments, manage healthcare plans, etc.
  • Employ big data analytics systems – These systems are expected to review large amounts of data to compare the effectiveness of treatments, aid medical discovery, analyze shifts in patterns of diseases and response to diseases, safety issues pertaining to healthcare equipment, etc. They rely on artificial intelligence for automatic correction of data inconsistencies and other chores such as extracting data from images, free text, etc.
  • Integrate APIs in healthcare – APIs allow developers to build applications quickly and protect patient data from malware and other malicious threats. They save storage space and allow users to pinpoint the exact source of data and get precise data. APIs are thus playing a pivotal role in alleviating clinical burden helping third-party apps and programs analyze data and enhance clinical decisions. As an integral part of healthcare, they now lead the way for successful interoperability.

Tread on the road to interoperability with Trigent

It’s easy to get lost in the shuffle, but with Trigent by your side, you can surely adopt best practices to shift your focus and achieve EHR/EMR interoperability. No matter how far you are on the road to interoperability, we will take you there with the necessary solutions. A few workflow changes and technologies should get us started.

Allow us to tell you how the new interoperability standards can help your practice. Call us today.

The Importance of Small data vs Big Data for Healthcare

Clinicians favor small data over big data for healthcare assessments and predictions. Here’s why

The healthcare sector is fragmented, complex, and hyper-local. There are over 100 healthcare systems in the US, 280 health information exchanges, and over 5500 hospitals. One million physicians are addressing the healthcare requirements of 320M Americans. While all these channels spit out data, leading to what is popularly known as big data for healthcare, there is another quiet and continuous flow of data from individuals or in this case patients, called `small data’.

Deborah Estrin, Computer Science at Cornell Tech and Healthcare Policy and Research at Weill Cornell Medicine puts it succinctly when she says, “Small data is being generated continuously on our mobile phones and through our online activities: walking and location patterns, as well as shopping, communicating, and web surfing. It is the various data traces we each generate every day, just by living our day-to-day routine: checking email, taking the bus to work, going grocery shopping, walking home, and more.”

Why clinicians prefer Small Data to Big Data for healthcare prediction models 

The big difference between big and small data is in big data large volumes of data are analyzed for patterns while small data looks at an individual’s historical data to develop models for predictions and futuristic treatment.

 

While big data has been at the forefront in healthcare technology for some time now, clinicians are often turning to small data to efficiently manage patient care. Small data helps them by providing quick input on allergies, times for blood cultures, missed appointments, and so forth, which are tactical in nature but extremely important inefficient patient care. Big data for example can say, X number of patients were admitted in the ER during a certain period of time. Can big data quickly identify how often or why Mr. or Mrs. John was admitted to the ER last month?

Small data is providing big insights for the individual. An app for managing pain for example quietly collects data about the individual, such as a fitness tracker, and can be presented to the individual and his clinician. In similar ways, smartphones can track heartbeats, eating habits, fitness quotient and you name it, to empower the clinician with insights into a person’s physical well-being.

The rising importance of Small Data in healthcare technology

Technology companies see the potential of smartphones in healthcare and innovative solutions are being unleashed. For healthcare ISVs, the challenge is to connect small data to big data, to improve individual healthcare, even as solutions are uncovered which can have a far greater impact on a larger target group. Not stopping there, the hidden challenge is to ensure privacy even as data that is collected is assessed and answers are uncovered.

Healthcare systems that have implemented electronic health records (EHR) can extend this to patients. If the systems can integrate individual health information, then both physicians and patients are maximizing digital health technologies.

Big Data Model  Small Data Model
What can be the effect of immunization programs? Is my child’s immunity to diseases taken care of?
Where do some of the healthiest people in the world live Is my diabetes medication working as expected
Are there any generic factors to identify a disease  Am I susceptible to X disease?

Some suggested systems include:

  • Health information exchange
  • Point-of-care decision support systems
  • Workflow tools to track and report on patient health
  • Smartphone and online appointment setting and registration.

Trigent Software understands the healthcare space having served a large number of clients over the last 20 years. Our commitment to patient healthcare has resulted in our focus on small data to improve the quality of patient care, reduce healthcare costs, and enhance patient loyalty. Call us today to know how we can enhance your patient-clinician relationships.

Reference:

https://onlinelibrary.wiley.com/doi/pdf/10.1111/jep.12350

https://research.cornell.edu/news-features/small-data-and-big-health-benefits