How data democratization can help solve healthcare’s problems

Many believe that AI and interoperability can play a significant role in enhancing data utilization in healthcare.

Every second of every day, hospitals, clinics and other healthcare institutions generate and mine massive amounts of data for patient care and management insights. From 60 percent to 80 percent of this information is unstructured, often locked away in incompatible systems.

For organizations without robust data management capabilities, these data silos create obstacles to using data effectively and delay access to vital patient information. The holdups can prevent timely medical interventions and impact outcomes adversely.

Data democratization, or expanding data usability and accessibility, can solve this challenge by improving platform interoperability. Democratization enhances access to information and system compatibility. Leveraging artificial intelligence to normalize, analyze and centralize data enables the delivery of insights at scale beyond any former capabilities.

Challenges, benefits of unstructured data

Unstructured healthcare data includes information that is not organized in a predefined and labeled manner, such as lab test results or a patient's birth year. This data comprises free-text fields in electronic health records (EHRs), clinical notes, emails and transcripts of audio recordings.

While difficult to access, unstructured data can be incredibly valuable because it contains nuanced details about patient care that structured data often misses. With the growing standardization of EHR data, more opportunities arise to organize and analyze unstructured data. By applying new technologies and protocols that align with emerging standards, healthcare providers can integrate and leverage the rich details in unstructured data.

Challenges presented by unstructured healthcare data include the following.

Lack of interoperability. The absence of interoperability between EHR systems presents a major hurdle. Healthcare has a fragmented EHR landscape, where different systems and standards coexist without integration or data sharing. This fragmentation prevents the smooth exchange of information, complicates patient data access, and hampers the delivery of coordinated and effective care.

Data underutilization. Healthcare employs some data standards, but unstructured data storage and processing varies across systems, limiting accessibility and use. Tools like natural language processing (NLP) and machine learning (ML) can extract valuable information. However, not all organizations can implement these tools effectively, causing potential insights to go unused.

Inadequate scalability. Managing large volumes of unstructured data demands significant storage and processing power, which can be costly and require advanced IT infrastructure.

Compliance and security risks. Protecting unstructured healthcare data is essential to comply with regulations such as HIPAA. Multiple formats make it difficult to implement security to protect sensitive information.

Addressing these challenges is critical for healthcare organizations to build trust in technology, increase efficiency, and improve quality of care through better data management and analytics.

Barriers to improving data access

In addition to the technical hurdles, operational and financial barriers prevent the efficient access and utilization of information.

Healthcare providers face substantial administrative burdens. Processes fueled by structured and unstructured data are time-consuming and add a layer of complexity to operations. While providers don’t perform these tasks directly, they manage the systems that organize and process information.

This indirect management can be arduous and often requires chart-chasing or digging through medical records to validate diagnoses — activities that are resource-intensive, expensive and difficult. The administrative programs become pivotal to the system's operational dynamics by incorporating nuances into the data management processes.

Financial impact

The overheads associated with data management are considerable. Healthcare organizations invest hundreds of millions of dollars in chart retrieval and other methods of managing often-redundant data across disparate systems. These activities incur high labor costs, leading to financial inefficiencies because of resource duplication and efforts wasted on unnecessary or repetitive tasks.

Distinguishing between the initial unlocking of data acquisition and the subsequent utilization of the insights this data can provide is crucial. The first step involves overcoming the barriers to access and effectively managing the data. Once accessed, the real value lies in analyzing this data to derive actionable insights, which can enhance patient care delivery.

Applying AI to healthcare data

AI works well when there is a massive amount of data that can be used to solve specific problems, and there is no better place that has a massive amount of data and has specific problems to solve than healthcare.

The industry has largely been slow to adopt technology, but it is moving faster than ever before with accepting AI — or at least making the effort to test it and see what it can do.

AI's role in healthcare goes beyond theoretical applications. It can use unstructured data along with structured components to predict patient conditions and enhance clinical decision-making. NLP and ML solutions can identify patterns in unstructured data — tasks not yet performed at scale. This capability enables healthcare providers to manage patient care more effectively and identify population health trends.

Despite its benefits, adopting AI and data democratization faces barriers. A common misconception is that AI can perfectly replace human judgment, overlooking its real value as an enhancer of efficiency and a supporter of clinical decision-making. Addressing these misconceptions and highlighting the complementary role of AI in healthcare is crucial for broader acceptance and implementation.

AI isn't intended to replace humans, but to improve efficiency. If you come at it from that perspective and you're using an AI product in healthcare, you really understand the value there. When you come at it from an “AI is perfect” perspective, you're more focused on a false positive prediction or some sort of hallucination that the model may have. You're not paying attention to the efficiencies that it drives.

Advantages of data democratization

Data democratization can revolutionize healthcare by boosting information sharing across platforms, payers and providers. Its use can improve patient outcomes, decrease the cost of care and empower physicians with better information at the point of care.

By making data more accessible to healthcare providers, organizations can better identify health trends and risks, offering predictive insights for improved and personalized care. Easy access to comprehensive data helps doctors make quicker and more accurate diagnoses and treatment plans. This leads to fewer mistakes and better overall healthcare.

Data democratization simplifies data management. Healthcare organizations can smooth their operations, eliminate unnecessary tasks and cut down on repetitive data handling. Democratization can deliver better administrative insights to help organizations run operations more efficiently. This saves money while enabling healthcare workers to spend more time caring for patients than doing paperwork.

AI enhances data sharing by quickly sorting through large amounts of unstructured data, bringing it together from different systems, and creating a unified view of patients. A large U.S. based healthcare system stated, “finding evidence embedded in charts used to take 30 to 60 takes less than 30 seconds."

Healthcare providers can quickly access patient information that was previously difficult to use, leading to more accurate diagnoses and better treatments.

Breaking down the barriers

By removing the obstacles to data access and using technologies like AI, healthcare can become more efficient, and clinicians can respond to patient needs faster. The industry must overcome interoperability, adoption and data security challenges to achieve this.

Current and upcoming regulations will standardize data formats and protocols, making sharing and integrating data across systems easier. Through effective acquisition and reuse of standardized data, healthcare providers can maximize the data’s utility to improve care delivery and operational efficiencies.

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