Do you remember the classic “Five W” journalism questions?: Who? What? Where? When? Why?
They are all questions that are often asked in healthcare, and the industry’s problem is that we often have answers, somewhere. But, getting those answers raises an entirely different question: What system is this in?
Healthcare has plenty of data today. Healthcare has big data. The problem is that it’s usually poor data: Not all of the necessary data is always captured, and the data that is captured is typically in silos. Plus, exchanging the data among systems to turn it into actionable information can be difficult and expensive.
We all live this reality every day: Healthcare has many different systems of record, and it’s not reconciled. The result is often a negative patient experience. Data that isn’t exchanged accurately and uncaptured data can have profound consequences in a hospital. Not entering the height and weight properly, or at all, in the EHR could lead to being prescribed the incorrect dosage. Not having a full and complete audit trail could result in reordering the same tests. Not taking the time to enter the patient’s allergies and current medications could lead to a harmful or even fatal reaction. You get the idea: Vital information in healthcare often fails to be captured, analyzed, and shared. It’s the Wild West of information.
There is no simple solution to this problem. Hospitals and health systems are taking steps to try to capture more data, glean insights from it, and share what is meaningful and actionable with the right people, but it’s easier said than done because healthcare’s data problem is actually a series of multifaceted problems:
1. Data is everywhere: Not only are there different source systems of data in the hospital, there are different source systems all over the organization—the clinical systems, like the EHR, ADT, LIS, and RIS, are just the tip of the iceberg. Data from other departments, such as HR and billing, is critical, too. New sources of data appear constantly with the rise of patient-generated information from monitoring devices like blood pressure sensors and wearables like Apple Watch® and Fitbit® that can track steps, heart rate, and more.
2. Data is complex: Data is a broad term and includes information that comes in many different formats: There’s text (patient has a broken arm) and images (the X-ray). There’s quantitative (vitals) and qualitative (physician notes). There’s clinical data (condition) and claims data (ICD-10 code). Even data that is captured in the EHR frequently isn’t standardized. Optimization efforts have begun to improve this: Standards such as HL7 and FHIR have begun to address the interoperability problem, but in some cases have created a data mapping nightmare of managing poor data exchange between systems. Scheduling becomes an issue as healthcare runs real-time, while data doesn’t necessary flow this way with current integration and methods. No matter how much standardization happens, the fact is a vast amount of the patient context is lost in conversation that never makes it into a system.
3. Data is evolving: Probably a more accurate statement would be that healthcare as an industry is evolving, and those evolutions have implications for how data is collected, stored, and analyzed. Regulatory and reporting requirements are continually increasing and changing, and everyone in the industry is waiting to see what effect the policies of the Trump administration will have. Additionally, the pivot to value-based care is driving hospitals and health systems to put more of a focus on data as they try to be more transparent to consumers about the price and quality of care at their facilities.
Healthcare data fuels interoperability and analytics, so you can see how data is truly at the center of everything. The health IT industry must do our part to help healthcare organizations figure out where this data lives and how we can extract it and share it with other systems to better inform both people and systems. Because, ultimately, better information can lead to improved patient outcomes.
At Spok, we’re in the business of streamlining workflows, and making it easier for clinicians to do their jobs. Data is naturally a big part of that. We’re working on monitoring what data is being exchanged (or not exchanged) in workflows today so we can make adjustments, and on adding and improving integrations among systems, so care teams can obtain the right information more easily and quickly. Stay tuned to see how Spok brings the context of the conversation to the table to solve data issues in the future.
What’s the data story at your organization? What area of progress are you most focused on right now? I’d love to hear from you!