Is it challenging to manage the outpatient queue at your hospital? Are your clinicians busy handling administrative tasks? Can traditional data be enough to drive enhanced health care delivery?
Here is how Artificial intelligence (AI) is one possible solution that can help address all these questions. From simple robotic process automation to advanced artificial intelligence and cognitive automation, the opportunities and outcomes are endless across all industries and the health care sector is no different.
Traditional health care analytics focused on the large amount of structured data available from clinical systems—pathology results, length of stay analysis, and morbidity and mortality statistics—but can only provide insight to provision health care to a limit. Deployment of AI techniques such as natural language processing (NLP) can give rise to reams of unstructured data buried in medical records.
This data can further help health care systems read and understand data to improve patient outcomes at every stage of care delivery.
Such data, when combined with the traditional data sets, can turn out be a game changer.
The health care sector is facing many challenges such as a growing and aging population with more long-term conditions whilst having to deliver health care more efficiently and economically. Clinician’s time being spent on administrative tasks only adds to the pressure and devalues the workforce. The National Health Service (NHS) in the United Kingdom has similar challenges and Deloitte is helping them address some of the productivity challenges like processing patient data and patients through the system as effectively as possible.
Nearly, a quarter of all NHS Trusts have a staff vacancy rate of over 15% while hospital admissions increased annually at an average of 3.6%. This clearly indicates the challenge faced by the NHS.
Today, there are AI tools with algorithms that can read doctor prescriptions, freeing up clinical time to focus on the frontline activities and reducing the burdened of administrative tasks. With an increase in appetite for automation, there is an interplay, or a blend of robotic and cognitive technologies, that automatically repeat processes, derive meaning of the data, and take meaningful action decided by the cognitive brain. The AI engines could read high quality cancer referrals, especially those with urgent suspicion, in a very predictable way, and put them in the order of priority.
However, it is the ability to look back in time and analyze the referrals from doctors that gives health care organizations the educational benefit.
It is imperative to understand that these technologies are not replacing clinicians or workforce but providing faster decision making to help health care systems focus on high value activities. It is also critical to manage privacy of the sensitive data pertaining to patients and clinicians.
It is now time for health care systems to start investing in artificial intelligence.