Healthcare can be confusing if not managed with utmost caution. From a patient visit to their lab examination to prescribing accurate medications— every process requires proper information regarding the patient’s health. However, doctors are not able to access the records of a patient sitting before them. They eventually get lost in multiple columns and rows on their screen.
So, where does the problem actually lie and how can physicians deal with every patient personally? The job of health IT was never to complicate the processes, but to make lives simpler for everyone. EHRs were one of the main reasons for creating complications, but once data troubles are taken care of, we can look forward to much better innovations that can simplify healthcare.
Like AI-assisted automation!
Automation brought a boom in, say, the automobile industry because it was able to drive up the operational efficiency multifold. What we need in healthcare is something similar— automation of sorts to expedite the processes for care teams and direct the focus of healthcare to enhance population health.
There is a huge sea of data and we just need to dive into it
Unlike most other sectors, healthcare is flooded with data which is mostly disconnected and disparate. The only challenge we are facing is that 80% of this data is unstructured and is distributed across various mediums such as claims, prescriptions, handwritten notes from physicians, and whatnot.
Too much data causes analysis-paralysis
Not just physicians but care coordinators, health coaches, and other members of care teams spend too much time shuffling through spreadsheets and putting information together. Connecting all these discrete information together is necessary to create a holistic picture of the patient. However, it is very difficult to seamlessly integrate and analyze them without any error.
All data at one place— seems interesting, promising, and achievable
In a survey, it was found that a physician spends over 18 minutes searching for patient data and documenting the progress reports, as compared to 16 minutes of face-to-face interaction time with the patient.
Currently, healthcare data resides in places way beyond the conventional sources such as EHRs or claims files. Likewise, data-related complications, too, are now beyond the capacity of conventional solving techniques.
What the US healthcare needs to do is to ensure true connectivity among different data systems— bring them on a single platform. While changing payment models are putting increased pressure on physicians to deliver point-of-care insights, bringing all data at a unified platform will solve their four major challenges:
- Poor data integrity
- Lack of ‘real’ interoperability among healthcare systems
- Poor communication and workflow misalignment
- Privacy and security-related challenges
Automation and machine learning— a new approach to drive up operational efficiency
The primary aim to bring automation and machine learning to healthcare is to provide the personalized care experience that it deserves. The growing importance of quality care has made it imperative to go beyond the typical consideration of regular data sources and rethink the way how healthcare organizations operate.
Physicians end up hopping from their EHRs to their analytics reports more often than not. They have a hard time leveraging the data they need to provide the care their patients deserve. Automation can help in solving this trouble, along with improving the population health. Prediction capabilities of automation combined with machine learning can tremendously help in making more informed decisions based on statistical evidence. We can reduce the burden of physicians and let the machine take care of the operational logistics so that they do not have to worry about revenue analytics.
- Making clean data available: Once every data is at one place, the next thing that should be aimed for is accuracy. Data should be clean and accurate to ensure that all the following procedures are accurate accordingly.
- Empowering providers with relevant insights: Most of the data we have, floating around in a network, needs to be put together with some additional insights to add more context to it. Machine learning would be able to solve this issue and deliver insights to physicians, just the way it’s needed. Care teams should get only the contextual information on their screens so that they cover every important aspect without spending a lot of time on the surrounding noise.
- Ensuring that the care-delivery process is proactive: Data generate insights, and insights have to be complemented with action. Machine learning and automation can show how many patients are at a high risk of going for an ED visit or a hospital readmission, but unless there is an intervention program associated with it to address them, the information is not efficiently impactful.
- Promoting better access to care: Physicians are overwhelmed with data around them, and machine learning and automation can ease that burden by identifying the at-risk patients and segregating them- pointing out the ones who need immediate care. In this way, care teams can target a specific patient population enabling them to plan timely interventions for every patient.
- Smart analytics to power every operational analysis: Delivering care to patients should not be at the cost of deteriorating healthcare organizations. With comprehensive data, leaders of the organizations can run smart analytics to gain operational insights in terms of opportunity and revenue enhancement and magnify their view into their activities and performance.
Can AI-assisted automation make healthcare simple again?
As sciences and industry are on the cusp of a data revolution, the birth of new data formats and databases has risen to an unprecedented level. We are at a stage where we need to begin considering the possibilities that healthcare automation brings to the table. From identifying the cause of the disease to predicting the best course of action, healthcare automation holds the key to every query a patient or a provider might have.
The case with patient-centric care is that physicians have to converge their efforts towards the patient— which means it’s imminent that physicians are updated about their collaborative actions as well as the patient’s health. The way physicians share details for transitional care management or for referrals can be greatly eased by automation and interoperability.
However, the most important aspect that automation in healthcare can help us achieve is the elimination of trial and error approach, eliminating the chances of mistakes and discrepancies in the operations.
Automation can make operational work hassle-free as it can:
- Create automated worklists for the staff
- Send alerts for efficient triaging
- Ensure timely access to care for each patient
- Take care of operational items and drive efficiency in the processes
- Reminders to patients for upcoming visits and medications
The road ahead
As health care has evolved to gain the ability to access large volumes of data with agility and ready access, the same principle if and when applied to automation and artificial intelligence can open up healthcare for a variety of innovations. Instead of limiting physicians to work with “sample” sets of data, today, they can be empowered to leverage massive sets of data without any restriction. It is time that we move from a generic ‘hypothesis-based’ approach to the ‘data-first’ approach. A legion of industries have benefited from machine learning and automation, and it is time that healthcare also looks for the same level of efficiency in terms of managing data and delivering proper care.
To know more on how you can leverage automation and artificial intelligence to deliver proper care and streamline your organization’s operations, get a demo.
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Join Team Innovaccer at booth #11 at NAACOS Fall 2018 Conference in Washington D.C. on 3 -5 October, and learn how you can power your data-driven initiatives with Innovaccer’s unified healthcare data platform.