Artificial Intelligence has been increasingly applied in healthcare due to the complexity and rise of data. There are many various forms of AI and a number of studies have already shown that AI can perform as well as or better than humans at key healthcare tasks. Many facets of patient care, as well as administrative processes within providers, payers, and pharmaceutical companies, may be transformed by these innovations.
Machine Learning as a type of Artificial Intelligence (AI)
Artificial intelligence (AI) is a set of technologies rather than a single one. The majority of these innovations have direct application in the healthcare sector, but the processes and tasks they support are diverse. Machine Learning (ML) is the one and the most common forms of AI technologies that are critical to healthcare. It is also divided into categories.
Machine Learning, Deep Learning, and Natural Language Processing Machine learning is the process by which computers are trained to ‘learn’ by exposing them to data.
- Machine learning is a subset of AI, and deep learning is a further subset of machine learning.
- Deep learning is a method for algorithms to learn to recognize hierarchies in data, allowing for complex data comprehension.
- Natural language processing (NLP) is a branch of machine learning that allows computers to analyze, extract, and interpret data organized within a language.
Machine Learning Applications and Benefits in Healthcare
Machine Learning (ML) has a plethora of applications and benefits in the healthcare sector. ML is now being used in hospitals to help streamline administrative procedures, map and manage infectious diseases, personalize medical treatments, etc. Let us take a look at some of the examples.
1. Patient Engagement and Adherence
Considered as the “last mile” problem of healthcare – patient engagement and adherence play a huge role in effective and good health outcomes. Noncompliance, or when a patient fails to obey a treatment plan or take prescribed medications as directed, is major issue. Even if healthcare providers use their clinical expertise to develop a treatment plan that will improve a chronic or acute condition, that often does not work if the patient fails to make behavioral adjustments, schedule a follow-up visit, comply with prescriptions.
In a survey of more than 300 clinical leaders and healthcare executives, more than 70% of the respondents reported having less than 50% of their patients highly engaged and 42% of respondents said less than 25% of their patients were highly engaged.
AI-based applications are effective in personalizing and contextualizing care that could result in deeper involvement by patients in order to achieve better health outcomes. Machine learning and business rules engines are increasingly being used to guide complex interventions around the care spectrum.
Another growing focus in healthcare is on effectively of Telehealth and Telemedicine to nudge patient behavior in a more anticipatory way based on real-time evidence. Through information provided by biosensors, smartwatches, IoMT medical devices, conversational interfaces and other applications, healthcare providers can tailor care delivery and coordinate with patients by analyzing AI-generated health data.
2. Diagnosis and Treatment
Years of medical training are required to correctly diagnose diseases. Even so, diagnostics can be a lengthy and time-consuming operation. The market for experts in many fields far outnumbers the available supply. This puts doctors under a lot of pressure, and it frequently causes life-saving patient diagnoses to be delayed.
Machine Learning algorithms, especially Deep Learning algorithms, have recently made significant advances in automatically diagnosing diseases, lowering the cost and increasing the accessibility of diagnostics. Machine Learning algorithms can learn to recognize patterns in the same way as doctors do. Algorithms, on the other hand, need a large number of specific examples – several thousands – in order to understand. With the thousands of patient data some hospitals have, this is particularly helpful in many areas where the diagnostic information a doctor examines is already digitized. Some examples include detecting lung cancer or strokes based on CT scans, identifying skin lesions in skin images, finding diabetic retinopathy signs in eye images, and using electrocardiograms and cardiac MRI images to assess the risk of sudden cardiac death or other heart diseases.
3. Healthcare Administration
In comparison to patient care, the use of AI in this domain has a lower potential for revolution, but it can still provide significant efficiencies. These are needed in healthcare because the average US nurse, for example, spends 25% of time on regulatory and administrative tasks. Robotic Process Automation (RPA) is the technology that is most likely to be applicable to this goal. It has a wide range of healthcare applications, including insurance processing, clinical documentation, sales cycle management, and medical records management.
Chatbots have also been used for patient engagement, mental health and wellbeing, and telehealth. Simple transactions, such as refilling prescriptions or making appointments, can benefit from these NLP-based applications. On the other hand, patients raised concerns about sharing personal information, addressing complicated health issues, and poor usability in a survey of 500 users of the top five chatbots used in healthcare. That is why it is important to choose a reliable and HIPAA-compliant partner.
Another AI technology with relevance to claims and payment administration is machine learning, which can be used for probabilistic matching of data across different databases. Insurers are responsible for ensuring that the millions of claims filed are accurate. All stakeholders – health insurers, governments, and suppliers – save time, resources, and effort by accurately recognizing, analyzing, and fixing coding problems and incorrect statements.
Machine Learning as a type of Artificial intelligence (AI) will play a significant role in future healthcare offerings. It is the primary capability behind the growth of precision medicine, which is generally acknowledged as a much-needed advancement in treatment. While early attempts at diagnosis and treatment recommendations have been difficult, AI will eventually master that domain as well. Speech and text recognition are now being used for tasks like patient contact and clinical note capture, and this trend will continue. Remote Patient Monitoring will be widely used for chronic disease management and acute care given the rapid advancement in AI for patient-generated health data analysis.
- Sharing and utilizing health data for AI applications. (2019). https://www.hhs.gov/sites/default/files/sharing-and-utilizing-health-data-for-ai-applications.pdf
- Using AI to Improve Electronic Health Records. Harvard Business Review. (2019, January 22). https://hbr.org/2018/12/using-ai-to-improve-electronic-health-records
- Kevin G. Volpp, M. D., & Namita Seth Mohta, M. D. (n.d.). Patient engagement survey: Improved engagement leads to better outcomes, but better tools are needed. NEJM Catalyst. https://catalyst.nejm.org/doi/full/10.1056/CAT.16.0842
- Berg, S. (2018, June 29). “Nudge Theory” Explored to Boost Medication Adherence. American Medical Association. http://www.ama-assn.org/delivering-care/patient-support-advocacy/nudge-theory-explored-boost-medication-adherence
- Utermohlen, K. (2018, April 17). 4 Robotic Process Automation (RPA) applications in the healthcare industry. Medium. https://email@example.com/4-robotic-process-automation-rpa-applications-in-the-healthcare-industry-4d449b24b613