In healthcare, we talk a lot about how we can use data to improve patient care. We discuss the importance of interoperability, the need for more data to be available to physicians, and how data can help physicians spot an issue with a patient they otherwise may not have caught.
What we don’t often discuss is the degree to which care settings can impact the need to effectively capture and make sense of data.
Take cancer, for example. While there are many different types of cancer and each treatment is varied, a patient that is being treated for cancer typically works with a single care team. In this scenario, it is important to have an accurate read on the patient and have as much data available as possible. Data helps the care team know if the patient is responding well to treatment, and helps determine next steps in the care plan.
What you won’t see in this well-coordinated scenario is the typical level of transition and hand-off between departments and care teams, which often leads to data loss.
Because, when it comes to data, we are hemorrhaging information during care transitions.
Imagine a different scenario: a patient is brought to a health system in critical condition with severe internal, orthopedic, and brain injuries after a motor vehicle accident. This care team could consist of the EMS (paramedics) team that brings the patient into the hospital, the emergency physicians that initially care for the patient in the hospital, the surgeons – trauma, orthopedic, neurosurgery – and anesthesiologists that work desperately to fix the damage in the operating room, the intensive care unit doctors helping the patient stay alive for the next several weeks, and then the rehabilitation therapists that help the patient resume a normal lifestyle. Every transition brings with it the chance that important information will be lost, and the number of critical care transitions dwarfs virtually every other care scenario. That makes the need to accurately capture data in critical care settings absolutely vital.
Gathering the Data without Developing Indigestion
From the perspective of healthcare organizations, two of our biggest goals in data gathering are minimal disruption to workflow, and matching data to patient behaviors. The reality is that humans can only monitor so much. While we’ve slowly made more data accessible to physicians over the past 25 years, its existence doesn’t necessarily make the data useful. As part of our normal workflows, we need technology that can help us aggregate the data in a way that makes sense and helps physicians determine the red flags that require the most attention.
There is a need for technology that addresses data-in-motion ranging from ‘in the moment’ to the ‘in-between’ – the transitions where the significant data, connections, and associations are lost. Without the support of technology, we are experiencing an unintentional loss of potentially valuable data that holds immense value as we move toward a precision medicine approach to patient care.
Losing Data Means Losing Out on Precision Medicine
We live in the age of chronic conditions: as of 2012, about half of all adults – 117 million people – in the US have one or more chronic health conditions. To manage this population of individuals, there is a significant need for us to move toward a precision medicine approach. However, we can only get so far if we constantly leave data on the table.
With today’s chronic condition climate, vitals can be interpreted very differently. For example, consider again the example of the accident victim taken to an emergency department. Vitals are taken, and the patient’s blood pressure comes back as ‘normal.’ What the treating physician doesn’t know is that this patient actually has a history of high blood pressure. A number that may be standard for the general population is actually low for this specific patient, and could be an indication that something is wrong internally.
Patients produce a lot of data, but we aren’t capturing it all. Having this information at a physician’s fingertips – particularly in critical care – can substantially change the course of treatment. Our other challenge: just because something can be monitored, is it essential? Without all of the data available for analysis through modern data science techniques, we won’t know.
Moving Forward: Predicting the Future State of the Patient
The ultimate goal for all healthcare stakeholders is to move from our current reactive approach to a proactive approach focused on informed predictions regarding the future state of the patient. Data-agnostic technologies can help us do this, and enable us to better predict when a patient’s condition will worsen – even when all current vital signs indicate a healthy patient.
If you know where to look, and you have technology solutions that can make it clear, the answer is almost always in the data.
Kevin R. Ward, MD is the Executive Director of the University of Michigan Center for Integrative Research in Critical Care and Professor of Emergency Medicine, University of Michigan Medical School. He also serves as the Executive Director of the University of Michigan Medical School’s Fast Forward Medical Innovation program.