The following is a guest blog post by Dave Corbin, CEO of HULFT.
Health care has a data problem. Vast quantities are generated but inefficiencies around sharing, retrieval, and integration have acute repercussions in an environment of squeezed budgets and growing patient demands.
The sensitive nature of much of the data being processed is a core issue. Confidential patient information has traditionally encouraged a ‘closed door’ approach to data management and an unease over hyper-accessibility to this information.
Compounding the challenge is the sheer scale and scope of the typical health care environment and myriad of departmental layers. The mix of new and legacy IT systems used for everything from billing records to patient tracking often means deep silos and poor data connections, the accumulative effect of which undermines decision-making. As delays become commonplace, this ongoing battle to coordinate disparate information manifests itself in many different ways in a busy hospital.
Optimizing bed occupancies – a data issue?
One example involves managing bed occupancy, a complex task which needs multiple players to be in the loop when it comes to the latest on a patient’s admission or discharge status. Anecdotal evidence points to a process often informed manually via feedback with competing information. Nurses at the end of their shift may report that a patient is about to be discharged, unaware that a doctor has since requested more tests to be carried out for that patient. As everyone is left waiting for the results from the laboratory, the planned changeover of beds is delayed with many knock-on effects, increasing congestion and costs and frustrating staff and patients in equal measure.
How data is managed becomes a critical factor in tackling the variations that creep into critical processes and resource utilization. In the example above, harnessing predictive modelling and data mining to forecast the number of patient discharges so that the number of beds available for the coming weeks can be estimated more accurately will no doubt become an increasingly mainstream option for the sector.
Predictive analytics is great and all, but first….
Before any of this can happen, health care organizations need a solid foundation of accessible and visible data which is centralized, intuitive, and easy to manage.
Providing a holistic approach to data transfer and integration, data logistics can help deliver security, compliance, and seamless connectivity speeding up the processing of large volumes of sensitive material such as electronic health records – the kind of data that simply cannot be lost. These can ensure the reliable and secure exchange of intelligence with outside health care vendors and partners.
For data outcomes, we’re calling for a new breed of data logistics that’s intuitive and easy to use. Monitoring interfaces which enable anyone with permission to access the network to see what integrations and transfers are running in real time with no requirement for programming or coding are the kind of intervention which opens the data management to a far wider section of an organization.
Collecting data across a network of multiple transfer and integration activities and putting it in a place where people can use, manage and manipulate becomes central to breaking down the barriers that have long compromised efficiencies in the health care sector.
HULFT works with health care organizations of all sizes to establish a strong back-end data infrastructure that make front-end advances possible. Learn how one medical technology pioneer used HULFT to drive operational efficiencies and improve quality assurance in this case study.
Dave Corbin is CEO of HULFT, a comprehensive data logistics platform that allows IT to find, secure, transform and move information at scale. HULFT is a proud sponsor of Health IT Expo, a practical innovation conference organized by Healthcare Scene. Find out more at hulftinc.com.