AI in Healthcare: Enhancing Health Outcomes and Patient Safety

The ultimate goal of any healthcare system is to ensure that people remain healthy and thrive outside of clinical settings. In the past, fitness trackers were the best AI aspect in wellness-related equipment. However, now, they have started to enter clinical care settings. AI is increasingly assisting healthcare professionals in improving health outcomes by enabling precise, faster, and accurate diagnoses, and delivering personalized treatments—all while maintaining a strong focus on patient safety. The integration of AI into healthcare in the recent past holds enormous potential. For example, AI embedded into modalities like MRI, CT, or, X-Ray provide real-time feedback to the technicians to position the patient precisely to avoid repeat scanning.

Screening, Triaging and diagnosis

A number of computational AI algorithms enable reconstruction of high-quality images with scans that are done faster with low dosages thus minimizing radiation exposure for the patients.  

While diagnostics is central to effective care, challenges persist as a result of growth in the use of medical imaging on one hand and workforce shortages on the other, potentially leading to incorrect or delayed treatment, thereby causing harm to patients. Intelligent algorithms integrated in radiology workflow can automatically determine and deliver the right case to the most appropriate available radiologists, based on their area of expertise, availability and current workload. This leads to efficient balance of caseloads thus accelerating reading times and corresponding faster diagnosis and treatments. Higher risk cases based on abnormalities can be prioritised in the review order to ensure the most urgent cases are not missed. Al enabled solutions can lighten the cognitive burden by analyzing vast amounts of medical data, imaging, and patient histories to provide a  summary for the Health Care Professional to act upon. Sophisticated computer vision machine learning models can detect patterns and anomalies that may be missed by the human eye, leading to more accurate diagnoses. AI-driven imaging tools can identify early signs of diseases such as cancer, significantly improving early detection and treatment outcomes.

In the future, AI can create personalized treatment, by analyzing historical data and current patient information, suggesting care plans for patients. It can do this even by considering  allergies and other nuances associated with each case. Predictive analytics play a crucial role in identifying high-risk patients, predicting adverse events and suggesting proactive interventions. This capability allows healthcare providers to address potential issues before they become critical, thereby improving patient outcomes and safety.

A rapidly growing body of research has demonstrated how AI can have a wide range of useful applications in healthcare, such as the interpretation of chest X-rays, spotting cancer in mammograms, identifying brain tumors in MR images, and detecting arrhythmias in ECGs. AI has also been used to inform cancer treatment recommendations based on a patient’s genetic profile and to predict the likelihood of complications in stroke treatment. With those applications comes the promise of earlier detection of disease, more precise diagnosis, and more personalised treatment – supporting healthcare professionals and patients across the continuum of care.  

Discharge and post-Discharge Surveillance

It is a well-established fact that recuperation of the patients can happen faster at home in the company of loved ones. Moreover, patients whose immune system is already impacted by an illness run a higher risk of contracting infections that are present in the hospital. So ideally the doctor may want to discharge the patient as fast as possible to ensure a speedy recovery and prevent exposure to pathogens at the hospitals. This could also ensure availability of beds for patients that are next-in line. However, determining the right moment to discharge a patient balancing the recovery, safety, re-admission risk and other considerations is a “tight-rope walking” exercise for the treating doctors. Here again, AI-based discharge readiness scores can aid the physicians in this discharge process both from ICU to ward and from ward to home.  

Surveillance of patients post discharge and risk stratification powered by AI can provide the assurance both to the doctors and patients alleviating some of the risks associated with the discharge process.

As a next frontier, AI will help connect previously disconnected and disparate patient data to provide novel insights that support healthcare providers in their decision-making. In cancer care, for example, AI can help integrate information across different clinical domains such as radiology, pathology, EHR systems, and genomics – providing a clear, intuitive view of the patient’s disease state. This can assist multidisciplinary tumor boards in making timely, informed treatment decisions, to give every patient the best chance of a positive treatment outcome. In the future, the intelligent integration of data could give further insight into a patient’s prognosis, supporting selection of the best care pathway for that particular patient based on an analysis of treatment outcomes for similar patients. As another example of AI supporting precision care, AI can analyze vital signs in acute and post-acute care to help care teams identify patients at risk of deterioration, allowing for timely intervention.

The future of AI in healthcare is promising, with emerging technologies and advancements poised to further reduce errors and enhance patient safety. Advanced predictive analytics, improved AI algorithms, and increased integration of AI in clinical workflows hold the potential to significantly transform healthcare delivery. As these technologies and tools evolve, the continued focus on mitigating diagnostic errors through AI will be crucial in achieving safer and more effective patient care.

To make the most of AI’s impact in healthcare, it is also important to overcome existing challenges around data management, lack of interoperability and maintaining data standards. These challenges can make it difficult to compile the necessary high-quality data for training AI models, particularly if those models rely on multimodal data from different sources. To overcome these challenges, robust and interconnected platform infrastructures are needed for collecting, combining and analyzing data at scale. As healthcare becomes increasingly distributed, extending from the hospital to the home, such infrastructures need to cover the entire continuum of care to connect patient data across settings and over time. For example, adoption of a robust platform can enable healthcare providers collect, compile, and analyse data from multiple sources, including medical records, imaging and monitoring data, as well as personal devices and technologies.  

Interoperability and standardized data sharing between different hospitals and health systems is key to exploiting the full potential of data and AI. Data should be available in formats that can be shared effortlessly, transparently, and securely. The use of open data standards and semantic interoperability, through methods such a unified Information Language System, can allow healthcare providers to connect and integrate data in a meaningful way. Finally, adherence to regional legislation and collaboration should enable the secure exchange and access to properly annotated data for AI research and clinical practice, while safeguarding patient privacy.  

Ajit Ashok Shenvi

---

Ajit Ashok Shenvi, Lead Data & AI Excellence, Philips Innovation Campus - Bengaluru