Healthcare Data Applications | Barriers to Adoption

Healthcare Data Applications | Barriers to Adoption

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Competitive Differentiation
  • Emerging Technologies

Publication | Update: Sep 2020

Biggest roadblocks for widespread use of big data include lack of data standardization, siloing, lack of accessibility, the need for a clinical data warehouse, and privacy/security concerns.

Healthcare IT has historically mirrored the enterprise IT sector, albeit on a multi-year lag. Because of this, we look to the evolution of big data within the enterprise sector to frame our outlook for big-data adoption in healthcare.

According to Stephanie Demko, Citi’s U.S. Healthcare Technology Analyst, the rise of enterprise big data has gained momentum in recent years given the elasticity of the cloud and the dramatic reduction of computing and storage costs paired with significant gains in processing power. However, this opportunity in itself is still in the very early innings, with a significant focus on data cleanup and usability. This suggests a long road ahead for big data to truly penetrate the healthcare sector given similar data siloing/usability issues within the healthcare sector as well as a slower shift to the cloud.

Additionally, healthcare IT faces several roadblocks that are unique to the space, given the competitive dynamics between the holders (and often times, vendors) of healthcare data. While in the near term, one of the biggest opportunities in enterprise big data is the consolidation/preparation of data for consumption by artificial intelligence and machine learning, this may get pushed off by healthcare IT vendors that are sensitive around releasing their data, which they view as key to their value.

Data Presents the Biggest Challenge

As Eric Schmidt, former Executive Chairman of Google, noted in his 2018 HIMSS (Healthcare Information and Management Systems Society Conference) conference speech this past March, several steps are required in order for providers and healthcare IT vendors to make use of their newly electronified data. To pave a path to improved healthcare cost/outcomes, vendors need to (1) move healthcare data stores to the cloud, (2) de-silo the data to create robust data sets, and (3) ultimately apply machine-learning models to improve predictive analytics and diagnoses. 

Echoing Schmidt’s keynote, we believe the biggest roadblocks facing widespread use of big data for artificial intelligent/machine learning applications are (1) lack of data standardization, (2) the current siloing of medical data, (3) lack of accessibility, (4) need for a ‘clinical data warehouse,’ and (5) privacy and security concerns.

Data Issue 1: Cleanup 

The lack of standardization across healthcare data presents the largest initial challenge to adopting big-data solutions. The vast amount of data generated and collected by a multitude of agents in healthcare today come in many different forms, both structured and unstructured — from insurance claims to physician notes within the medical record, images from patient scans, conversations about health in social media, and information from wearables and other monitoring devices.

For the most part, we believe healthcare data are compiled in SQL-based relational databases because these are well suited for discretely codified billing and clinical transactions data. However, this format presents limitations in both the volume and the velocity of data processing, while rapid growth in unstructured health data could create the need for hybrid (relational and NoSQL) databases.

Further, the data-collecting community is equally heterogeneous, making the extraction and integration of the data a real challenge. Providers, payers, employers, disease-management companies, wellness facilities and programs, personalized-genetic-testing companies, social media, and patients themselves all collect data. Even standardizing for end market and form, the data standards presented in Meaningful Use were more open to interpretation than a standardized protocol, creating a lack of standardization even across EHR systems. Integration of data will require collaboration and leadership from both the public and private sectors. 

Data Issue 2: Siloing 

Medical data are spread across many sources governed by different states, hospitals, and administrative departments and information silos exist across both private and public sectors. Even within organizations themselves, multiple sources of data — such as clinical, financial, and operational data — are kept separated.

The issue is further compounded by each data system’s unique key identifiers, validation rules, and format. With medical data siloed in a multitude of verticals, the result is difficulty in data aggregation when attempting to create a complete data set to analyze a patient or a population. The integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other.

Data Issue 3: Accessibility

Stemming from the siloing of medical data, the ability to create full data sets for one patient or a population to work with is limited by the lack of accessibility across different source of data. According to an athenahealth survey, while 79% of doctors believe that having all available patient data in one place is critical to their jobs, only 14% could access EMR information across different departments, patient care centers, etc., even within the same hospital. While recent regulation looks to improve upon data sharing, our channel checks have shown vendor hesitation and proactive friction in data sharing.

In order to increase interoperability among hospitals, physicians, and other relevant parties, the industry is slowly shifting to a new technology known as FHIR (Faster

Healthcare Interoperability Resources). FHIR creates standards for different data elements so that developers can build application programming interfaces (APIs) that can be used to access datasets from different systems.

Data Issue 4: Warehousing

Finding a place to warehouse the huge amount of data in healthcare is a challenge. Assuming the data get standardized and become de-siloed and accessible for use, the challenge standing in the way is the need for a clinical data warehouse to host the vast amount of data (projected data size 2,310 exabytes by 2020). Only once this data are curated into usable data sets can they then be used for sophisticated analysis with a rich API. The data warehouse would require two tiers of data, with the first tier being primary data stores sourced from EHRs, supplemented by a second tier comprising unstructured data collected from everywhere else.

Data Issue 5: Privacy/Security

Safeguarding data is key and heightens the cost of data vs. other industries. Given the enormity of total population medical data both in value and volume, large data stores are at a high risk of tampering and theft. This is particularly vital as a leak of identified health data is irreversible, unlike the leaking of a more dynamic data asset such as a consumer’s credit card information.

This security risk necessitates significant investments to safeguard the data, creating a heightened level of cost compared with other industries. Privacy concerns have also led to slower momentum in data storage evolution, with locally hosted systems still prevalent within healthcare due to perceived cloud risk.

Consumer Health Informatics (CHI) applications

When it comes to Consumer Health Informatics (CHI) applications barriers can be divided into two groups: system-level and individual level barriers. System-level barriers can further be divided into technical and healthcare system barriers. Technical barriers refer to usability, work flow issues and data security concern. Healthcare system barriers include reimbursement system and incompatibility between legacy system in healthcare institutions and patient applications. The individual level barriers are directed towards the consumer or the clinician. Consumer issues cover problems like lack of access to application, privacy concern, knowledge and limited literacy. Clinician issues affect consumer choice and with the negative attitudes of clinicians may be a barrier to consumers’ use.

The Table below summarizes the barriers faced by consumers in leveraging CHI application health self-management.


Source: Health Science Journal:”Barriers to Adoption of Consumer Health Informatics Applications for Health Self Management”, Kumar Laxman, Sharanie Banu Krishnan and Jaspaljeet Singh Dhillon



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