From Fragmented to Coordinated: The Big Data Challenge

Posted on November 27, 2018 I Written By

The following is a guest blog post by Patty Sheridan, MBA, RHIA, FAHIMA; SVP, Life Sciences at Ciox.

When healthcare organizations have access to as much data as possible, that translates into improved coordination and quality of care, reduced costs for patients, payers and providers, and more efficient medical care. Yet, there is a void in the healthcare data landscape when it comes to securing the right information to make the right decision at the right time. It is becoming increasingly critical to ensure that providers understand data and are able to properly utilize it. Technologies are emerging today that can help deliver a full picture of a patient’s health data, which can lead to more consistent care and the development of improved therapies by helping providers derive better insights from clinical data.

Across the country, patient data resides across multiple systems, and in a variety of structured and unstructured formats. The lack of interoperability makes it difficult for organizations to have access to the data they need to run programs that are critical to patient care. Often, various departments within an organization seek the same information and request it separately and repeatedly, leading to a fragmented picture of a patient’s health status.

Managing Complexity, Inside and Out

While analytics tools work well within select facilities and research communities, these vast data sets and the useful information within them are very complex, especially when combined with data sets from outside organizations. The current state of data illiquidity even makes it challenging to seamlessly share and use data within an organization.

For example, in the life sciences arena, disease staging is often the foundation needed to identify a sample of patients and to link to other relevant data which is then abstracted and mined for real world use; yet clinical and patient reported data is rarely documented in a consistent manner in EHRs. Not only do providers often equivocate and contradict their own documentation, but EHR conventions also promote errors in the documentation of diagnostic findings. Much of the documentation can be found in unstructured EHR notes that require a combination of abstraction and clinician review to determine the data’s relevance.

Improved Interoperability, Improved Outcomes

Problems with EHR interoperability continue to obstruct care coordination, health data exchange and clinical efficiency. EHRs are designed and developed to support patient care delivery but, in today’s world of value-based care, the current state of EHR interoperability is insufficient at best.

Consider the difficulty in collecting a broad medical data set. The three largest EHRs combined still corner less than one-third of the market, and there are hundreds of active EHR vendors across the healthcare landscape, each bringing its own unique approach to the information transfer equation. Because many hospitals use more than one EHR, tracking down records for a single patient at a single hospital often requires connecting to multiple systems. To collect a broader population data set would require ubiquitous connection to all of the hundreds of EHR vendors across the country.

The quality integration of health data systems is essential for patients with chronic conditions, for example. Patients with more serious illnesses often require engagement with several specialists, which means it is particularly important that the findings and data from each specialist are succinctly and properly communicated to fellow doctors and care providers.

Leveraging Technology

As the industry matures in its use of data, emerging technologies are beginning to break down information road blocks. Retrieving, digitizing and delivering medical records is a complex endeavor, and technology must be layered within all operations to streamline data acquisition and make executable data available at scale, securing population-level data more quickly and affordably.

When planning to take advantage of new advanced technologies, seek a vendor partner that provides a mix of traditional and emerging technologies, including robotic process automation (RPA), computer vision, natural language processing (NLP) and machine learning. All of these technologies serve vital functions:

  • RPA can be used to streamline manually intensive and repetitive systematic tasks, increasing the speed and quality at which clinical and administrative data are retrieved from the various end-point EHRs and specialty systems.
  • NLP and neural networks can analyze the large volume of images and text received to extract, organize and provide context to coded content, dealing with ambiguous data and packaging the information in an agreed-upon standard.
  • With machine learning, an augmented workforce can be equipped to increase the quality of records digitization and the continuous learning across the ecosystem, where every touchpoint is a learning opportunity.

Smarter, faster and more qualitative systems of information exchange will soon be the catalysts that lead paradigm-shifting improvements in the U.S. care ecosystem, such as:

  • Arming doctors with relevant information about patients
  • Increasing claims accuracy and accelerating providers’ payments
  • Empowering universities and research organizations with timely, accurate and clinically relevant data sets
  • Correlating epidemics with the preparedness of field teams
  • Alerting pharmacists with counter-interaction warnings

Ultimately, improving information exchange will enable healthcare industry professionals to elevate patient safety and quality, reduce medical and coding errors tenfold and enhance operational efficiencies by providing the relevant data needed to quickly define treatment.

Achieving this paradigm shift depends almost entirely on taking the necessary steps to adopt these emerging technologies and drive a systematic redesign of many of our operations and systems. Only then will we access the insights necessary to truly impact the quality of care across the healthcare landscape.

About Ciox
Ciox, a health technology company and proud sponsor of Healthcare Scene, is dedicated to significantly improving U.S. health outcomes by transforming clinical data into actionable insights. Combined with an unmatched network offering ubiquitous access to healthcare data, Ciox’s expertise, relationships, technology and scale allow for the extraction of insights from structured and unstructured clinical data to create value for healthcare stakeholders. Through its HealthSource technology platform, which includes solutions for data acquisition, release of information, clinical coding, data abstraction, and analytics, Ciox helps clients securely and consistently solve the last mile challenges in clinical interoperability. Ciox improves data management and sharing by modernizing workflows and increasing the accuracy and flow of information, while providing transparency across the healthcare ecosystem and helping clients manage disparate medical records. Learn more at www.ciox.com.