Once obscure research tools, medical devices today are nearly ubiquitous in healthcare. An acutely ill patient, for example, can be monitored by dozens of medical devices, creating thousands of data points daily.
Historically, medical device data has been isolated or trapped in silos. It has unique communication protocols, physical connections, update rates and terminology. Key advances have put medical devices on the precipice of an evolutionary leap from charting and documentation to active patient monitoring and intervention.
One area of increasing risk is managing patients on opioids, particularly those patients who are afflicted with obstructive sleep apnea. Continuous monitoring of these patients has become a growing recommendation.
For hospitals and health systems, especially those breaking ground on a net-new medical device integration (MDI) program, the formidable task list requires the input and expertise of a project team. Ideally this should be comprised of leadership from myriad departments, including IT, networking, facilities, clinical staff and biomedical engineering.
This team will be responsible for every phase of deployment: acquisition, rollout, implementation and transition to live operations. The team will determine the hospital’s objectives and integration goals, as well as the devices, device types, business and clinical requirements, risk management concerns, patient safety goals and costs.
Tracked through multivariate, temporally-trended information, clinicians can apply historical and real-time data to facilitate clinical decision-making that is based upon changing and evolving trends.
This reinforces the need to have a comprehensive and forward-looking approach to selecting an MDI and middleware provider that can support the technical and clinical needs of a healthcare organization.
Middleware can be leveraged to pull data from medical devices and combine it with other data in medical records to create a more holistic and complete picture of a current patient’s state. Combining analysis with real-time data at the point of collection creates a powerful tool for prediction and decision support.
This raises critical questions that pertain to patient safety and the level of risk assumed by the hospital. How do patient documentation needs differ from real-time patient intervention needs? What is real-time data flow, and what is not?
Because data used for real-time intervention, such as clinical alarms, impacts patient safety, any delay in its delivery to correct individuals can have deleterious effects. Thus, it is important to understand the implications of requirements on data delivery latency, response and integrity.
The capabilities of various middleware solutions overlap, but there are basic architectural and regulatory considerations that must be considered outside of the specifics of software or physical access to data.
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