The great conundrum of data liquidity in healthcare (2024)

by Paddy Padmanabhan

Opinion

Feb 28, 2019

Electronic Health RecordsHealthcare IndustryMarkets

A new HHS ruling aims to push healthcare towards greater data interoperability. However, data liquidity will face a host of challenges.

The great conundrum of data liquidity in healthcare (1)

Credit: Nikada / Getty Images

This past weekend, I ran an experiment on my personal medical records. I went online to myChart, the patient portal from Epic that hospitals and physicians use to communicate with patients and document medical encounters, test results, and so on. I was looking for a way to share my medical records with a family member. I found an option within the health section of the home page menu to “Share My Record.”

When I clicked, it asked for the name of the individual I wanted to share the record with, and after I keyed it in, a temporary code (valid for five minutes) was provided. I had to share that code with the recipient who then had to go on another site to key in that code to get an electronic copy of my medical record. After five minutes, the access was shut off.

At the recently concluded HIMSS19, the oxygen was sucked out of the giant Orlando Convention Center by one topic: data. More specifically, who owns it, who gets to access it, and how they get to use it. The agenda for the conference was fixed the moment a proposed 800-page ruling by the Department of Health and Human Resources (HHS) was made public before the conference began. The rule aims to advance interoperability, specifically targeting healthcare programs administered by the Centers for Medicare and Medicaid Services (CMS) such as the Medicare Fee-For-Service program (FFS), Medicare Advantage program, Medicaid Managed Care Organizations (MCO), and several others that account for a significant percentage of the overall healthcare spending in the U.S.

The rule focuses on providing patients unfettered and no-cost access to their medical records to enable them to make informed healthcare decisions. One of the major issues in data interoperability has been the lack of clarity around who owns patient data. At the HIMSS event, CMS administrator Seema Verma made it clear that it was the patients who owned their data, calling it an “epic misunderstanding” among healthcare providers and technology companies, specifically electronic health record (EHR) vendors who had hitherto thought otherwise.

In “Healthcare IT’s ‘Lord of the Rings’ problem,” I discussed the current state of interoperability and the predominant focus on the exchange of clinical data among healthcare providers as well as their technology vendors. The HHS ruling expands the debate to cover health insurance companies, acknowledging that claims and encounter data sitting in payer systems can provide enrollees with a more complete picture of their medical history.

The intent of the ruling is to allow healthcare consumers to have the data follow them around as they moved from provider to provider or payer to payer. In addition, the ruling requires all data to be exposed through standard application programming interfaces (API) for the developer community to build user-friendly applications that can potentially help consumers access their data through devices and applications of their choice.

A related issue that the ruling addresses is price transparency for healthcare procedures and outcomes.

While the ruling goes into exhaustive detail on a number of topics including data blocking and cybersecurity, let’s take a quick look at how the ruling could impact three broad stakeholder categories in the healthcare ecosystem.

Patients

Enabling patients with access to their own medical information is a giant step towards improved healthcare outcomes, not least because an informed consumer is in a better position to manage his or her healthcare. However, the lack of a unique patient identifier (UPI) that hinders with patient data matching, and lack of standardization, especially at the semantic layer (“speaking the same language”), are significant challenges to implement the interoperability vision outlined in the HHS ruling. In addition, patient data may not be up to date. When I looked at my own medical records online, I found that my provider had not updated it since 2017. It’s not much use to have access to all the data if the data is not current.

Clinicians and hospitals

Ensuring patients have ready access to their medical records, regardless of where they may have received care, is one of the key goals behind the HHS ruling. Due to the fragmented nature of the healthcare sector and the traditional lack of co-operation between providers and payers, when it comes to sharing data, this is a significant challenge today.

Still, some organizations, such as Premera Blue Cross in Washington State, have found creative solutions by partnering with a local Health Information Exchange (HIE). The HIE aggregates patient medical information across the state and makes it available through a single API. Premera aggregates the data into their member database and makes it accessible back to providers so that members are instantly recognized whenever they present their insurance card at any provider location. According to Colt Courtright, Director of Corporate Data and Analytics at Premera, HIE’s are getting more traction today and are a huge accelerator to obtaining EMR data.

Payers

The HHS ruling requires health plans to make their member health information available to members seamlessly as they change providers, plans and employers. Here again, Premera’s model could be an example of how to accomplish this by partnering with a local HIE such that the information is readily accessible by members and their healthcare providers at any time. Despite issues with semantic interoperability, Courtright states, “to be able to interpret and put our own data into provider workflows in a seamless way is a win for payers.”

As new data sources come online and as non-traditional players enter the market, the need for data liquidity for real-time access to a comprehensive view of patients will drive interoperability efforts. Related challenges include data management on cloud and on-premise environments, ensuring veracity of parties involved in data exchange, and semantic interoperability will become front and center in the pursuit of data liquidity. The growing acceptance of FHIR as the standard for exchanging health information electronically now means that the “VHS-Betamax fight” is over and industry participants can focus on adding more content on the data model.

As for my personal medical records, my healthcare provider is yet to respond to me on when I can expect my 2018 medical records to be updated in their system.

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The great conundrum of data liquidity in healthcare (2024)

FAQs

What is data liquidity in healthcare? ›

Another is that when one creates “more ways and more choices for patients to own their computable health data thus enabling patients to use their data to get help and advice”, this is known as Data Liquidity. [ 4]

What are the problems with healthcare data security? ›

Four Most Common Healthcare Data Threats

Even though there are many threats to healthcare data, the four most common include phishing, ransomware attacks, data breaches, and DDoS attacks.

How has big data impacted healthcare? ›

Benefits of big data in healthcare

Create a holistic, 360-degree view of consumers, patients, and physicians. Improve care personalization and efficiency with comprehensive patient profiles. Identify geographic markets with a high potential for growth.

What are the factors that contribute to poor data quality in a healthcare database? ›

There are five main causes of poor data quality; Data Entry Errors, Incomplete Data, Duplicate Data, Outdated Data and a Lack of Data Standards.

What does data liquidity mean? ›

Data liquidity is the ability for data to flow, securely. Poorly interconnected systems are the enemy of data liquidity.

What is liquidity and why is it important? ›

Liquidity refers to how easily or efficiently cash can be obtained to pay bills and other short-term obligations. Assets that can be readily sold, like stocks and bonds, are also considered to be liquid (although cash is, of course, the most liquid asset of all).

What are the three biggest data challenges in healthcare today? ›

Exploring the intersection of big data and healthcare problems reveals critical barriers to leveraging technology for improved patient outcomes.
  • Data quality and integration. ...
  • Data privacy and security. ...
  • Data standardization issues. ...
  • Data storage and transfers. ...
  • Data structure issues. ...
  • Infrastructure and scalability.
Mar 14, 2024

What is the biggest threat to the security of healthcare data? ›

Ransomware is the biggest threat to the security of healthcare data.

What is the #1 cause of healthcare data breaches? ›

Hacking and IT incidents have consistently been the most common type of breach, and the number of healthcare data hacking cases increases each year mainly due to ransomware attacks.

What are the cons of big data in healthcare? ›

Challenges of Using Big Data In Healthcare

Data Security: One of the biggest limitations of big data in healthcare is the security of data. From cybersecurity breaches to malware and phishing attacks, health data is vulnerable, confidential information. Hospitals must prioritize the security of data.

How does data quality affect healthcare? ›

Examples of poor data quality in healthcare

Inaccurate or Incomplete Patient Information: Data entry errors or incomplete documentation can lead to incorrect patient information, such as incorrect names, addresses, or medical history. This can have serious implications for patient care and treatment.

Why is data important in healthcare? ›

When healthcare providers have access to a complete picture of a patient's health history, they can offer a thorough treatment plan. If the patient data is not shared between doctors, researchers, and hospitals, it holds back the development and can cost lives.

Why is healthcare data difficult? ›

The data is structured and unstructured.

Thus, unstructured data capture is often allowed to appease the frustrated EMR users and avoid hindering the care delivery process. As a result, much of the data captured in this manner is difficult to aggregate and analyze in any consistent manner.

How to improve data quality in healthcare? ›

How to ensure data quality in healthcare? 10 Steps to follow
  1. Establish a data governance framework.
  2. Develop data quality policies and standards.
  3. Assess current data quality.
  4. Implement data quality improvement initiatives.
  5. Invest in data quality tools and technologies.
  6. Enhance data capture and entry processes.

What is bad data quality? ›

Bad data quality refers to moments when data is inaccurate, missing, or otherwise unreliable, leading to what is termed as data downtime, which can adversely impact business operations and decision-making. According to our annual survey, incidents of data downtime nearly doubled year over year.

What liquid data means? ›

Liquid Data is a virtual data layer in the sense that, except for caching, data is not kept in any Liquid Data or WebLogic component. Instead, data services dynamically retrieve data from the physical data sources. Dynamic access ensures that applications have access to current (that is, real time) information.

What are the three types of liquidity? ›

We discuss the notion of liquidity and liquidity risk within the financial system. We distinguish between three different liquidity types, central bank liquidity, funding and market liquidity and their relevant risks.

What is the liquidity of a hospital? ›

Liquidity position of a hospital measures its ability to pay its cash obligations as they are due. Not being able to meet these obligations could result in bankruptcy and financial distress (Garanina and Belova, 2015).

What are the three basic measures of liquidity? ›

Current, quick, and cash ratios are most commonly used to measure liquidity.

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