Is IoT the missing link for operational and clinical improvements in healthcare?
Value-based healthcare, personalized medicine and patient centricity will quickly reshape how medical care is delivered and paid for. An important force behind this trend is the connectivity enabled by the Internet of Things (IoT), which wirelessly links people, IT systems and things and enables two-way transfers of data through a network.
All this can happen effortlessly without human-to-human or human-to-computer interaction.
IoT in healthcare enables us to connect a multitude of people, things with smart sensors (such as wearables and medical devices) and environments.
Sensors in IoT devices and connected smart assets can capture patient vitals and other data in real time – then data analytics technologies, including machine learning and artificial intelligence (AI), can be used to realize the promise of value-based care.
For example, significant value can be gained through:
- Operational improvements, which boost operational efficiencies in ways that enhance quality of care while reducing costs.
- Clinical improvements, which enable faster and more accurate diagnoses and a more patient-centric, scientific determination of the best therapeutic approach to support better health outcomes.
Let’s take a closer look at the potential for IoT in healthcare – also referred to as the Internet of Medical Things (IoMT) – to achieve true, value-based care.
“IoT in healthcare can dramatically optimize workflow and staffing.”
Once modeled, this data can help staffing managers improve workflows and make better staffing and scheduling decisions. The data can also be used to understand the movement of people and assets and predict where staffing and equipment will be needed most the next day or in the weeks ahead.
Ideally, healthcare facilities will move to appropriate dynamic, on-demand scheduling and resource allocation schemes. This would ensure the right people are assigned to the right places to efficiently deliver quality care while improving staff morale and patient satisfaction.
Using IoT in healthcare, we can finally begin to tackle the critical problem of alert fatigue in clinical care delivery. This occurs when care providers receive too many clinical alerts – with up to 99 percent of them being false alarms. Alert fatigue is directly responsible for growing numbers of patient injuries and deaths.
With IoT in healthcare, there are many ways to improve patient care and safety. For example, hospitals can use smart, connected monitoring devices linked to patient records, pharmacy systems, room location, nursing staff schedules and more.
The sensors in these smart devices collect data, which is integrated with other medical device and system data and then analyzed to determine whether to trigger a silent alarm for a noncritical event or an audible alarm for a life-critical event. In this way, IoT will increase confidence in alarms, reduce workload and drive timely action – keeping patients safer.
“ With IoT, clinicians make faster, more accurate diagnoses and more precise, personalized treatment plans. ”
IoT in healthcare technologies can integrate and analyze diverse types of diagnostically relevant data and move it to clinical decision-support systems.
Healthcare providers using these systems will have a more complete picture of each patient’s health, as well as the tools to make faster and more precise treatment recommendations. Such opportunities are already realized in the diagnosis and treatment of sepsis, where speed and accuracy are critical to saving patients’ lives.
IoT in healthcare: Better patient outcomes, lower costs
These are all examples of how IoT in healthcare allows us to collect granular patient data at frequencies previously unimaginable – not just when people are sick or in a hospital but where people live and work. This data can be combined with behavioral, physiological, biochemical, genetic, genomic and epigenetic data and more.
The volume and scope of the data will make it possible to develop powerful learning and adaptive diagnostic and therapeutic models. Over time, these algorithms will detect new, previously hidden or unknown patterns and relationships between data, diagnoses, treatments and patient outcomes.
The ultimate goal is next-generation expert systems that will eventually develop a level of autonomy in diagnosis and treatment.
Soon, we’ll see them routinely assisting physicians and nurse practitioners, helping them provide high-quality care and achieve better patient outcomes at lower costs.
This article first appeared on SAS Insights and was republished with permission.
Every morning, wake up to the blog that gives you the latest trends shaping tomorrow.