Predictive Analytics in Healthcare: Utilizing AI to Anticipate and Minimize Adverse Health Events

Predictive Analytics in Healthcare: Utilizing AI to Anticipate and Minimize Adverse Health Events
Narendra Vankar, Co-Founder, Quantum CorpHealth Pvt. Ltd - Predictive Analytics in Healthcare
This article has been contributed by Dr Narendra Vankar, Co-Founder, Quantum CorpHealth Pvt. Ltd.

India’s health landscape is rapidly evolving. We are moving towards the best possible medical care with experienced, trained doctors; new advancements in medical technology; and the introduction of personalized, AI-based healthcare, perfectly tailored to fit a patient’s symptoms and health history. Medical professionals can now use AI tech to anticipate and mitigate health issues, from the minor to the adverse. AI is able to make the jobs of medical professionals easier by analyzing vast amounts of data, while predictive AI can identify patterns and predict patient outcomes. Predictive AI helps in intervening in a timely, proactive manner to anticipate and minimize adverse health events. Here we explore the pros, cons, and future prospects of predictive AI in healthcare.

Applications
Benefits
Challenges

Applications

  • Disease Prevention and Early Intervention: The first and foremost way that Predictive Analytics can help in healthcare is for disease prevention and early intervention. The earlier the diagnosis, the better the chances for recovery and survival. By analyzing patient data like medical records, genetic data, and lifestyle factors, predictive AI can determine the onset or progression of a disease, enabling medical professionals to provide their patients with information on changes in lifestyle, preventative medication, and early screenings to prevent the onset or progression of a disease.
  • Personalized Treatment Planning: Genetic information and biomarkers are key to being able to make personalized treatments for patients. Predictive AI makes this much easier to do by predicting patient outcomes based on this information, and their reactions to potential treatment paths. Predictive AI can optimize outcomes and minimize adverse side effects and complications.
  • Hospital Resource Management: A major concern in hospital management is perfect record-keeping and effortless, patient-friendly operations. AI can help by forecasting patient demand, optimizing bed occupancy, and identifying bottlenecks in the workflow like availability of ORs and other medical processes like MRI and CT scans. Predictive AI allows hospitals to effectively allocate resources, like staffing and equipment, to ensure a smooth and easy patient experience. This is especially helpful for patients and loved ones when they are already worried about their health and wellness.
  • Population Health Management: Not only can AI predict and analyze data on an individual level, but they can also analyze the data of specific population pockets, identifying high-risk communities, predicting health outcomes, and target interventions. This can reduce the overall cost of healthcare for individuals and improve accessibility, especially for communities that are below the poverty line. This can also help medical providers organize disease management programs, reducing the risk of hospital readmissions, illness complications and exacerbations.

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Benefits

As we have seen through the previous points, Predictive AI can have many benefits for medical professionals and patients alike. Here, we explore them in more detail.

  • Early disease detection and prevention can help to identify individuals who are at high-risk of diseases like diabetes and heart disease. Not only does genetic predisposition play a part in this, but also lifestyle factors and medical history. Early measures to mitigate and reduce the likelihood or adverse effects of the disease can be taken by the medical provider to give their patients a long and healthy life.
  • Nowadays, individuals are looking to healthcare providers to give them an individualized treatment plan, tailor made to their exact specifications. No longer does the one-size-fits-all method of treatment work. Predictive AI can determine the most effective treatments per the patient’s medical history, lifestyle, and genetics. Medical professionals do not need to try and test methods of cure any longer.
  • As a result of these individualized treatment plans, predictive AI can be indirectly responsible for improved patient outcomes. Medication adjustments, lifestyle modifications, and behavioral interventions can improve patient outcomes and satisfactions, and also improve quality of life for at-risk patients.
  • Last but not the least, predictive AI can benefit both patients and medical providers at an operational level. For medical institutions, predictive AI can help effectively allocate resources that save time and money, giving patients a smooth and stress-free experience. Patients can also save their costs due to personalized and proven treatment plans, and not spend additionally on unnecessary scans and medications.
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Challenges

While there are clearly many benefits of Predictive AI, we also have to keep in mind the challenges of integrating technology into our medical processes. As with any improvements, it is time-consuming and can be confusing. To make informed choices we must be aware of both the pros and cons of any new advancements.

  • Firstly, the healthcare system in India is in disarray, and this has been especially proven by the Covid-19 pandemic. Analysis of data is easy when all the data is in one place, but healthcare records in India are fragmented at best and unfinished at worst. Inaccurate and incomplete data can lead to biased and inaccurate predictions, compromising the effectiveness of the predictive model.
  • Second, there is always the question of data privacy and security when we talk about data and AI models. Protecting patient data and privacy is paramount, and medical institutions must take utmost care to safeguard this data and adhere to strict regulations when using predictive AI.
  • Due to the novelty and familiarity of this technology, predictive AI is still not fully trained to cover the entire spectrum of human data. Therefore, some of their predictions can be biased, leading to unfair or discriminatory outcomes. It is the responsibility of the medical professional to determine whether the AI driven outcome will work for their patient.
  • Lastly, as humans we may become over reliant on this technology, leading to depersonalization when dealing with patients. It is easy to forget in the amount of patients that doctors see that each patient is dealing with an unfamiliar and scary situation, and it is important to retain the human touch and sensitivity that AI cannot recreate.

In conclusion, predictive AI holds great promise in the med-tech and healthcare field. While keeping in mind the numerous benefits that predictive AI has, we must also keep in mind the challenges, and strive to achieve a balance between the two, making the patient the center of attention. Patient-centric treatment and care is the responsibility and first priority of every doctor, and should not be affected regardless of any new technologies being introduced.


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