How The Cloud Is Improving Healthcare Data Interoperability

| Author , tagged in healthcare data interoperability, cloud-native healthcare technology, healthcare data management
Cloudticity, L.L.C.

Healthcare providers generate a tremendous amount of data. Hospitals and doctors’ offices use computer systems for managing patient information such as records, tracking, and billing. All of these systems need to communicate with each other when they receive new information or when they wish to retrieve information.

This exchange of massive data sets and sensitive information creates huge challenges. In order to understand where we are today with healthcare data solutions, it’s helpful to take a look back at how the industry has evolved.

The History Of Healthcare Data Management

Ever since the 1970s, when the healthcare industry started to rely more on computers, it has been necessary to standardize data formats so doctors could share patient information with each other clearly and consistently. This inspired the development of HL7 standards.

HL7 Healthcare Standards

Health Level-7 (HL7) standards were created by Health Level Seven International, a nonprofit organization dedicated to developing standards for the exchange of electronic healthcare data.

HL7 standards provide a framework for the exchange, integration, sharing, and retrieval of electronic health information. These standards define how information is packaged and communicated, setting the language, structure, and data types required for seamless integration among systems.

HL7 helped fill the need for international healthcare standards, but technology has evolved since HL7 v2 was introduced in 1989, creating new challenges and opportunities.

Congress Passes The HITECH Act

Fast forward to the 21st century. The healthcare industry has grown exponentially and providers have continued to implement new technologies, creating the need to make sharing information more efficient and secure.

In response, Congress passed the Health Information Technology for Economic and Clinical Health Act (HITECH) in 2009. The act promoted and expanded the adoption of health information technology, specifically, the use of electronic health records (EHRs) by healthcare providers. This would become a significant driver for growing the healthcare data industry.

Prior to the HITECH Act, only 10% of hospitals had adopted EHRs. While many healthcare providers wanted to transition to EHRs from paper records, the cost of making the change was high.

To mitigate pushback, the HITECH Act introduced incentives to encourage hospitals and other healthcare providers to make the change. According to HIPAA Journal, 86% of office-based physicians had adopted EHRs and 96% of non-federal acute care hospitals had implemented certified health IT by 2017.

Challenges And Opportunities With Digital Data

The HITECH Act sparked an unprecedented growth of digital health data. Healthcare IT companies saw the challenges that the new law presented as an opportunity. Several launched products to help facilitate the exchange of healthcare data. While these products have helped, they have also presented issues.

Aside from the high cost, these products also became outdated because they were created for use with data centers and not the cloud. Many of the systems require significant administration and upkeep, so scalability is also an issue. But yet again, these new challenges presented new opportunities.

Leveraging The Cloud

Up until this point, companies have been innovating based on what came before. Most built new products on top of legacy integration engines. Innovating in this manner works for a while, but eventually you need to meet market demands and create new solutions from a blank slate. That is exactly what has happened in the healthcare industry.

HIPAA-Compliant Healthcare Interoperability API

With this need to “innovate innovation” in mind, the team at Cloudticity threw away the old models and focused on building a modern solution for healthcare data interoperability. We wanted to be able to ingest vast quantities of healthcare messages at incredible scale with zero physical  infrastructure, so we knew the solution needed to be cloud-native.

Our goal was to create something better than the tools already in use. We wanted to make sure the tool gleaned actionable insights (using methods such as business intelligence and machine learning); maintained a high level of performance; and was extremely secure and accessible to all providers.

The tool would have to be able to handle thousands of messages per second while remaining affordable and scalable. With the needs of the industry in mind, we created a first-of-its-kind architecture that helps providers quickly deploy and manage a healthcare HIPAA-compliant interoperability RESTFul API on Amazon API Gateway. We have tested the API in four use cases.

Use Case 1: Reducing Readmissions

When Medicare patients are readmitted, healthcare providers don’t get paid, so reducing readmissions clearly increases revenue. Since one of the most common causes of patient readmissions is congestive heart failure, we analyzed millions of records for congestive heart failure cases and built a machine learning model using that data.

When new patients were admitted for congestive heart failure, the machine learning tool analyzed their records and compared them to historical data. This technology was able to determine the probability that a patient would be readmitted to the hospital, allowing healthcare providers to take appropriate actions to lower that probability.

Providers identified the patients at high risk of readmission and assigned them case workers upon admission, not discharge, leading to a significant reduction in readmissions. We were able to drive workflow changes that decreased readmissions, translating directly to higher revenues.

Use Case 2: Increasing The Efficacy Of Clinical Decisions

Determining which medications will be best for each patient is related to that person’s genetic makeup. We identified an opportunity to link genomic data with clinical data, enabling more effective treatment.

By using machine learning coupled with extremely large and inexpensive cloud-based data storage, our architecture gives providers the ability to match a patient’s symptoms and genomics with millions of other similar cases. Simply put, these tools enable better clinical decision-making. This translates to healthier patients and more efficient use of time for healthcare providers.

Use Case 3: Improving The Patient Experience

Aside from treatment efficacy, providers must also focus on the experience patients have when they walk through the door, starting in the waiting room. With our system, everything is tracked from the time a patient arrives to the time they leave. When a patient arrives, it triggers a message in the hospital’s patient information system. When the patient sees the doctor, it triggers another message.

This transparency gives providers an amazing advantage because they can see in real time how long each phase of the visit lasts. The hospital can then make adjustments to decrease wait times and create a more efficient visit, translating to better patient experiences.

Use Case 4: Driving Additional Revenue

Many small daily procedures or treatment actions are never billed, which adds up to a significant amount of lost revenue over time.

For example, when hospital physicians do rounds, they are moving quickly because they have to see numerous patients in a short amount of time. They may provide medication to a patient, such as giving a Tylenol to someone with a headache. This action needs to be coded and billed, but many times it is not.

We built a system that identifies the physician’s clinical notes and codes them for billing, capturing additional revenue.

By leveraging the cloud, we can help healthcare organizations tackle the challenge of data interoperability at scale in unique ways that are significantly less complex than the traditional approach of using integration engines.

Cloudticity’s healthcare data interoperability tool is currently processing tens of millions of messages a day at major health organizations. This entirely cloud-native solution is improving performance, revenue, and security, all at a 90% cost reduction compared to non-cloud-native tools.

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TAGGED: healthcare data interoperability cloud-native healthcare technology healthcare data management

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