Leveraging AI in Hospital Management
By Anish Gupta
Embracing AI will enable doctors, hospital staff, insurers, researchers and drug discovery departments to have intelligent insights supported by data
In general, artificial intelligence refers to technology that is capable of performing tasks intelligently, similar to those performed by humans. There is, however, some difference between artificial intelligence in healthcare and AI in other fields. The reason is that AI is working to support doctors, diagnosticians and other medical professionals, and help enhance their overall productivity as opposed to just carrying out automation of tasks. AI in healthcare holds the potential to assist medical professionals across the spectrum to generate more valuable insights, deliver improved patient care, and eventually, accelerate treatments for terminal illnesses.
Due to its ability to process massive amounts of data, healthcare AI systems can be used for a variety of tasks such as identifying patterns in medical records, predicting patient outcomes, and providing personalised treatment recommendations. Using artificial intelligence in healthcare will improve the current processes and systems by enabling everyone—doctors, hospital staff, insurers, researchers and drug discovery departments in pharmaceutical companies—to have intelligent insights supported by data.
Prominent Trends
The healthcare AI market size is worth billions of dollars and is poised to grow in the years to come and this is only being made possible because of the rapid growth of new and emerging technologies and trends in AI-enabled healthcare. In my opinion, below are the two most prominent trends that are likely to dominate the future AI healthcare ecosystem.
Machine learning and AI for medical data analysis
The healthcare systems in developing countries have very few Electronic Medical Records (EMR) solutions being used. To begin with, the challenge comprises digitising handwritten clinical notes and prescriptions. Using an EMR in its pure form can enable the digitisation of information. However, the implementation of EMR requires a considerable amount of time because data entry takes time. Thus, healthcare costs remain high and so do patient burdens. AI-based solutions can be developed to read handwritten notes or turn voice into text, Suki is one such example. An AI-powered voice-enabled digital assistant for doctors, Suki is a solution to provide administrative assistance to US healthcare organisations.
As these and other applications of AI in healthcare become more widespread, it is likely that the industry will continue to see significant cost savings and improvements in patient care. As a follow-up to the digitisation problem, we have to figure out how to analyse the semi-structured data that is contained in EMRs to derive meaningful insights. We must correlate data across every step of the patient's care journey and then generate insights for the doctors to use to make quick and accurate decisions. With advanced NLP algorithms based on AI, this can be accomplished.
The concept of personalised medicine and care
In the past, medicines and treatment plans have been designed in a one-size-fits-all manner. However, AI and other digital technologies are causing this to change. With the aid of genomics and artificial intelligence, medications are now being tailored to meet the specific needs of each patient. In addition, treatment plans and care management are becoming increasingly personalised based on lifestyle, demographics, patients' behaviour and social determinants of health. For example, a partnership between Novo Nordisk and Glooko drug companies has led to the development of a customised diabetes care management plan that assists patients in managing their glucose levels and medication compliance.
Future-proofing Hospitals
Cost reduction is going to be a major challenge for hospitals in the future. This is evident in the way reimbursements to hospitals are priced as part of the Ayushman Bharat scheme. Initiatives like this are admirable, however, they require hospitals to bear a significant burden of cost reductions and shoulder a major chunk of risk when it comes to making losses. What’s more is that private insurance companies are currently underwriting policyholders at an increasing rate because people want health insurance after COVID. But once these policyholders start to claim for their hospitalisations, insurance companies will soon see an effect on their bottom line and they will have to start working with hospitals to tighten their contracts and reduce outflow.
This is where care management will come into play: Hospitals can leverage care management, especially post-discharge, which can then help them to optimise the length of stay the patient has in the hospital. This way they can keep the cost low and not get into losses while meeting these requirements.
Also, while keeping costs low is definitely important, hospitals have another challenge of managing competition which can be direct i.e. from other hospitals, and indirect i.e. from other digital health players. Effective care management and engagement are important to build patient loyalty, they help create a proactive approach and facilitate an understanding of the patient’s needs and wants outside of the hospital. This will help hospitals to improve patient health outcomes and this directly reflects on the quality of care that they provide.
Thus, as we move toward the value-based care healthcare ecosystem, the risks will be shared between the hospitals and insurance companies. While you may say that we may have a long way to go about that in India, the foundation of it starts with effective care management. Hospitals should start to proactively manage their patients so that they can improve patient health outcomes which will be important not only for their reputation but also for the payments/incentives that they get from the insurance companies.
Financial and Operational Efficiency
AI and ML are changing the way healthcare works, both financially and operationally. One of the most significant changes has been in the way that patient data is collected and managed. Previously, medical records were scattered across different systems, making it hard to get a full picture of a patient's health. Now AI and ML-powered tools are helping to change that by aggregating data from multiple sources and organising it so it's easier to use and access. This is leading to better decision-making by doctors and other healthcare professionals. In addition to that, AI and ML tools are also helping streamline billing and insurance. By automating these tasks, AI & ML are freeing up time for healthcare workers to focus on more important tasks.
Also, using AI and machine learning to generate insights from unstructured data enables physicians to have the right information at the top of the patient's record to make an accurate diagnosis. These insights assist in the personalisation of the entire care management process for the patient. AI can also help diagnosticians to improve turnaround times for reports, leading to further speeding up the diagnostic process and making it more accurate. Thus, by leveraging these technologies, healthcare providers can begin to address some of the most pressing challenges facing the healthcare ecosystem today.
Author:
Mr. Anish Gupta,
Head of Products and Insights, Heaps.ai