How AI Is Transforming Healthcare and Improving Patient Outcomes

The numerous milestones crossed by Artificial Intelligence (AI) have brought the world on its toes. The level of its growth in various industries has been extremely fast-paced and sometimes, completely unpredictable as well.

Of all the industries it has cast its spell upon so far, the modern Healthcare industry has been of prime importance. Our generation has witnessed a complete shift of paradigm in the way patients are treated by doctors as they now have huge amounts of data in their hands, a considerable amount of which can be put to good use.

AI is transforming the Healthcare industry like never before.

Traditional analytic methods have been revamped and there has been a change in the clinical decision-making techniques on account of the data volumes going up at a staggering rate. This enables the decision-makers to gain unparalleled insights while making a diagnosis, deciding upon treatment variability, planning the care process, and finally, patient outcomes.

A recent analysis by Accenture states that key AI applications in Healthcare can create an annual savings of $150 billion dollars for the US Healthcare economy by 2026.

Artificial Intelligence surely is the game-changer in the Healthcare industry quite easily. How? Let us look at a few ways in which AI has transformed Healthcare and improved patient outcomes to gain better understanding of the same.

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Medical Imaging Diagnostics

From automating workflows to improving processing speed and image quality, medical imaging developers are discovering numerous ways to use AI in Healthcare for detecting and diagnosing diseases.

While top-grade imaging can seem expensive on the outside, in the long run, it saves a huge amount of capital spent on hospital stays and averts the more substantial, invasive disease treatment that would be required if the disease was detected towards a later stage.

Advances in medical imaging have largely contributed to improved accuracy of screenings for disease, aiding in early diagnoses. Without these advances, it would be impossible to diagnose these ailments until they reached a more life-threatening stage where symptoms become evident. With the advent of molecular imaging, disease can now be detected right at the cellular level. This, in turn, leads to more accurate treatment and far better patient outcomes.

Scattershot disease treatment is now a thing of the past. Modern imaging diagnostic techniques allow physicians to treat you precisely and artfully, targeting the disease straightaway. Medical imaging allows your physician to make evidence-based, accurate judgments regarding patient care. This leads to better recovery outcomes and decreased mortality as well as morbidity.

AI-Powered Electronic Health Records

The National Center for Health Statistics conducted a national survey wherein close to 75% of providers believed that their EHRs allow them to offer improved patient care. EHR systems do more than plainly reserve and convey health data. They also leverage the information for the profit of the patients.

The most promising application happens to be that of using AI to make existing EHR systems more intelligent and flexible. Through information provided by provider EHR systems, watches, software can tailor recommendations by comparing patient data to other effective treatment pathways for similar cohorts. The recommendations can be provided to providers, patients, nurses, call-center agents or care delivery coordinators.

While AI is being applied in EHR systems principally to improve data discovery and extraction and personalize treatment recommendations, it poses far greater potential when it comes to making EHRs more user-friendly.

EHRs are often cited as contributing to clinician burnout. The use of virtual medical assistants along the lines of Siri or Alexa for the EHRs can make retrieval of information convenient for the practitioners. AI-based speech to text input can significantly bring down the clinical workload of filling data into the EHRs, thus reducing clinician burnout. AI could help EHRs continuously adapt to users’ preferences, improving both clinical outcomes as well as clinicians’ quality of life.

Virtual Health Assistance

Researchers are developing AI-driven virtual assistants for the Healthcare industry. Increased access to smart devices, improved technology, consumer expectations, the transition to value-based care, and evolving state and federal policies are all driving virtual health adoption in the United States at a rapid speed.

Evidence is soaring that virtual health assistance can ameliorate outcomes in specific populations by lessening the span of hospital stays, decreasing readmission rates, and improving the experience among chronically ill patients. The combination of AI with Healthcare wearables is a potent one that helps streamline telemedicine and is thus improving the patient outcomes.

Next, IT developed a Healthcare tool that supplements both Healthcare professionals and patients alike with a reciprocative approach to improve medication adherence and overall compliance.

This tool happens to be the first artificially intelligent “Virtual Health Assistant” built for the management of chronic diseases. It has been designed in a way that it helps patients follow their treatment programs for chronic diseases. It collects information about the patient’s day-to-day activities between doctor visits. It can also perform at-home patient health screenings and send the results to the health care provider to discuss during the next scheduled appointment.

A number of Healthcare organizations have already devised a Virtual health strategy, but the elements of these strategies differ greatly. Now is suitably the right time for health systems to put the right strategies and infrastructure in place and help fuel the idea of integrating appropriate platforms and technologies into the care delivery model.

Robotic Assistance

Robots equipped with Artificial Intelligence are greatly assisting microsurgical procedures to help lower surgeon variations that could have an adverse effect on patient recovery. Following the successful outcome of AI-assisted surgery in the past few years, experts have quoted that they expect to see more robot-aided procedures in the years to follow.

In fall of 2017, Maastricht University Medical Center in the Netherlands made use of an AI-assisted surgery robot to stitch together tiny blood vessels – some up to .08 millimeters across, and no larger than .03 millimeters.

They said the operation was performed on a patient who was suffering from lymphedema, a chronic condition where fluid gets accumulated and causes swelling (a common side effect of breast cancer treatment). Microsurgery is a comparatively new and potentially better treatment for the condition. The procedure includes connecting blood vessels to lymphatic vessels to reinstate the flow of lymphatic fluid and reduce the swelling.

The robotic system used in the operation was controlled by a surgeon, whose hand movements were converted into smaller, more precise movements that were then performed by a set of “robot hands. The device also made use of AI to balance any tremors in the surgeon’s movements, to ensure the robot performed the procedure correctly. According to the medical center, the AI-assisted surgery went well, and the patient recovered soon after.

Patient outcomes are rapidly advancing as health systems accumulate and integrate data into their operations through Artificial Intelligence. Machine Learning techniques and Advanced Analytics are being used simultaneously to help expose critical insights and best practices from the millions of data elements associated with robotic-assistance.

Proactive Medical Care

In conventional medical treatment, treating the patient after the disease has been detected was an active trend. For example, if a patient visits a doctor with certain symptoms, the doctor might conduct certain tests, and later discover the patient has cancer. Treatment like radiation and chemotherapy are started thereafter. Likewise, if a patient goes to a doctor with symptoms of diabetes, the doctor first conducts the tests before prescribing insulin shots. This kind of treatment is called Reactive medical care.

With Artificial Intelligence, a significant shift has been observed in this trend since reactive medical care became proactive medical care. In the latter, the patient’s complete medical history is first studied, after which high-risk markers for various diseases are highlighted. At-risk patients are then watched for any change in their conditions. If anything seems alarming enough, then the application can suggest medical intervention.

There are apps that propel the patient to be an active participant in their own health scenario. For example, the app, PeerWell encourages people to take a proactive role in their health, by saying “here are some things you can do today to improve your health and provide a good outcome”, and provide suggestions.

Similarly, there are condition-specific applications for AI like congenital heart disease, palliative care, and diabetes management. The idea is to make technology to help the patient do most of the things, and avoid having to wait for a doctor to perform those tasks for them.

Conclusion

AI is transforming the Healthcare industry like never before. It is changing the role of the doctors, just as it is changing the role of the patient. There most definitely are certain challenges that still need addressing, however, the benefits outweigh them, and AI is here to grow and advance. It will change the medical word — in treatment,  in diagnosis, in disease detection, in treatment disciplines and much more.

Read more: RPA in Healthcare: The Key to Scaling Operational Efficiency