Why Healthcare Urgently Needs Machine Learning at the Point of Care

An average visit to the physician lasts somewhere between 13 to 16 minutes. That’s how long your physician has to see you, assess your health issues, provide diagnoses, and send you out on the path to wellness.

This isn’t much time if you think about it. Looking up a patient’s record in the EHR, putting together data and insights relevant to the patient’s health issue, listening to the patient’s concerns, and conducting the basic examination (“say aaaaah,” “breathe in,” “that’s great, now breathe out”)- 16 minutes is hardly enough!

So how will physicians cope the sea of data and deliver the care their patients need?

Bringing machine learning to the rescue

Machine learning has become a part of everyday life for many Americans- from navigation app driving estimates to iTunes recommendations to Amazon’s “You might also like” (and mostly, we do). But in healthcare, the use of machine learning has so far been limited to research projects in large academic institutions.  

Machine learning is when a computer has been taught to recognize patterns buried under data and process it the way humans do. As we switch to a new world of quality, value-based care, healthcare organizations need to be able to draw more insights and conclusions from bits of healthcare data. And machine learning might just be the way to help healthcare make smarter decisions.

The power to transform care

The aim of bringing machine learning to healthcare is to provide that personalized experience of care which healthcare seeks. The growing importance of quality in care has made it imperative that we are able to plan and deliver patient-centric care, ultimately leading to better population health, enhanced patient satisfaction, and reduced cost of care.

• Making insights relevant to the provider: Data and analytics healthcare organizations currently have are cool- but it would be much cooler if they are in a way the end user wants and can use. Half the data we have floating around in a network needs to be put together with some additional insight to add more context to it. Machine learning would be greatly able to solve this issue and deliver insights to physicians, just the way it’s needed.
• Becoming proactive with care delivery: Insights have to be complemented with action. Machine learning and analytics can show how many patients are at the greatest risk of an ED visit or a hospital readmission, but unless there is an intervention program in place to address them, the information is only partly impactful. 
• Improving access to care: In today’s quality-oriented healthcare, equity and access to care are fundamentally important. Physicians are overwhelmed with data around them and machine learning has to ease the burden by stratifying patient population, picking the ones that need prior attention, and smoothening the care continuum.

But, insights are only worthwhile when available at the right time

Apart from delivering insights in an actionable way, it’s important that they are available to the right set of people at the right time. Having easy access to a patient’s risk score, blood pressure, their diagnoses could be instrumental in saving time and planning better interventions.

While changing payment models are putting increased pressure on physicians to have instant access to actionable information about their patients, healthcare organizations are still struggling to deliver point-of-care insights that could revolutionize the healthcare works. Poor data integrity, a lack of interoperability among healthcare systems, and challenges with privacy and security are the leading reasons why insights are not available to physicians at the point of care.

Actionable information at the point of care: a game changer for patient-centric care

Most of the healthcare organizations depend on the EHRs they have for storing and retrieving data. In certain cases, organizations have a data integration or analytics platform to deliver insights. But in all these cases, the physician remains disconnected, looking to gather information.

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 power of machine learning can be immensely utilized in helping physicians acquire data. Physicians spend about hours on EHR data entry during a typical 11.4-hour workday, looking for patient data or keying in information in their EHRs, and here’s how real-time insights can ensure patients be healthier:

• Physicians can access critical insights about the patient such as their risk score, their diagnoses, the screenings that are due for them, the measures that were missed, and the codes that were dropped. These insights will provide a better context to the physician as they treat the patient.

• Information such as the patient’s PCP, the care coaches, the details of the last encounter, and more could be helpful in creating a better picture of the patient and keeping the care team updated.

• The way physicians share patient details for transitional care management or for referrals can be greatly eased by machine learning and interoperability, simplifying how patients navigate across the care continuum.

• Intelligent processes can also reduce workflow challenges, helping physicians find and plug the gaps in care right at the point of care- and push the information back in their EHRs without even having to lift a finger.

• Most important of all, we can eliminate the process of trial and error, and base decisions on evidence with information at the fingertips and increased focus on patient-centric care.

• Delivering insights at the point of care can help physicians cut down that time and focus better on their patients.

• These applications can also be pre-populated with information based on the usage patterns and deliver reminders and surface key insights.

The road ahead

We have begun the transition from ‘the digital age’ to ‘the age of intelligence.’ We now have capabilities that did not even exist a decade ago. We’re still scratching the surface and it may sound futuristic, but the need for it can not be overlooked. It’s important that providers are aware with what’s going on with their patients, where they are, where they might be going, and how they could help them as they proceed to a new system. When these systems can gather, remember, and deliver what works best for each physician and patient, they can make healthcare truly efficient.

To know more on how you can deliver better insights at the point-of-care with a unified healthcare data platform, get a demo.

For more updates, subscribe.