Data in Healthcare: Challenges and Future Prospects

There's power in data. In healthcare, predictive analytics can revolutionize clinical care and decision-making. However, healthcare systems are also grappling with a challenge that hinders this evolution - the struggle to harness and utilize data quickly and effectively.

Where We Are

Imagine that you have a powerful telescope that can view far-distant galaxies. Now, imagine that you are using this telescope in a city where the skies are always cloudy. (London, maybe?)

The potential for discovery is immense, but poor viewing conditions limit what you can observe.

Here are a few examples of "cloudy skies" in healthcare that can prevent AI from achieving its promise:

  1. Fragmented data: Currently, healthcare institutions depend on data that is often fragmented and inconsistent, arising from a lack of standardized processes and collection and management systems. This variability complicates the data retrieval process and affects the data's reliability and comparability.

  2. Unclean data: Healthcare data often contains errors, duplications, and inconsistencies, necessitating extensive and repetitive cleansing that drains time and resources.

  3. Nonstandardized data: Integrating and comparing data across systems and departments is nearly impossible without standardized data formats, limiting comprehensive analysis and decision-making. Here, we are concerned about units and data elements as simple as "time," which may be expressed in multiple time zones.

  4. Intensive data gathering: Healthcare professionals often sift through several thousands of data tables to find information for a specific solution. This labor-intensive process is time-consuming and prone to errors, further complicating data usability.
Where We're Going

Where We're Going

Despite these challenges, the future of healthcare data is promising. Advances in data analytics, machine learning, and AI are creating new avenues for effective healthcare data implementation. At Fullsteam Health, we have undertaken the task of ensuring that healthcare organizations have access to curated and actionable data, solving the problems above.

Here's how:

  1. Data standardization: Automated processes to generate standardized data formats significantly reduce data fragmentation and inconsistencies.

  2. Data management: Advanced data management mechanisms will streamline the data cleaning and integration process, making it more efficient and less resource-intensive.

  3. Concept groupings: There needs to be consistency in clinical and administrative concept groupings. You need to know all the analyte values that represent "creatinine," the vitals that represent "blood pressure," and the bed IDs that currently and historically have been represented as "intensive care unit." Our Data Curation platform analyzes historical and metadata and new raw names that enter the system, ensuring that concept groupings are constructed and maintained over time.

  4. Notifications fit for your workflow: With Fullsteam Health, you do not have to change your workflow. Our multi-modal notification engine sends data insights directly to your organization's desired workflow.

A culture that values and understands the importance of data in healthcare is crucial. Having curated and actionable data for real-time and historical use cases increases the development and deployment of solutions. By addressing current issues that prevent leveraging AI broadly, healthcare systems can unlock the true value of data, leading to innovative treatments and improved patient care.

The future of healthcare, empowered by data, is not just a possibility but an achievable goal.