Here's how you can use data analytics to revolutionize patient care in healthcare.

We can try and define a maturity curve for healthcare personalization: Level 1 (Basic): Limited data, standard treatments (think one-size-fits-all medication). Level 2 (Intermediate): Uses basic data (demographics, allergies) for adjustments and targeted recommendations. Level 3 (Advanced): Integrates wearables, lab results, and potentially genetics for personalized treatment based on real-time data. Level 4 (Future): Advanced AI tailors care with real-time adjustments based on continuous data analysis.

AI and Analytics with Identity MDM is critical to segmenting patient populations while harnessing SDoH data, then stratifying them based on risk in the ER or OR, to proactively identify care gaps, craft personalized care protocols and then coordinating care as well as transitions based on risk, to improve the quality of care delivery and deliver superior patient outcomes, while lowering cost as well as risk of non-compliance (30-60-90 day re-admission rates, ALOS, CAUTI, CLABSI etc.). If done well, this could maximize value-based care (VBC) reimbursements for providers and augment their revenue and margins.

Proactively leveraging SDoH data into Predictive Analytics enabled by Machine Learning, Deep Learning and NLP can help healthcare providers stratify patients based on risk and personalize their care protocols based on care gaps to seamless manage care transitions across the care continuum to ensure superior patient outcomes while releasing capacity for the most valuable assets in the hospital - the ER and the OR.

Predictive analysis uses historical and real-time data to forecast future health events, enabling proactive healthcare management. By identifying patterns and risk factors, healthcare providers can intervene early, potentially preventing disease progression and reducing emergency hospitalizations. For instance, predictive models have been successfully used to anticipate readmission risks in patients with chronic conditions like heart failure, allowing for timely interventions and improved patient management. This approach not only enhances patient outcomes but also optimizes healthcare resource utilization .

Off contract buying or "Maverick Buying" is rampant in most hospitals with physicians, nurses and care givers purchasing expensive medical devices and drugs from their manufacturers of choice, off contract, often paying 50-100% over contracted prices leading to avoidable and unnecessary expenses that impact the healthcare organization's bottom line. Barnes Jewish Healthcare (BJC Healthcare) used self-service analytics to track maverick buying across its facilities against monthly targets and saved over $ 212 MM over a 4 year period, earning them the Gartner Supply Chain Excellence Award in 2015.

Leveraging AI (machine learning and deep learning) to scan thousands of DICOM images to proactively identify potential cardiovascular or neurological issues for Radiologists is a compelling way to drive efficiencies and also reduce Radiologists' fatigue and burnout, and is being leveraged by industry thought leaders in healthcare today. As well Medical Robotics are being leveraged to unobtrusively scan customers entering retail stores, hospitals and pharmacies to scan temperature in the post-COVID 19 era across APAC to manage risk.

AI and data analytics significantly improve diagnostic accuracy by analyzing complex medical data, such as imaging and lab results. Advanced algorithms can detect patterns and anomalies that may be missed by human eyes, leading to earlier and more accurate diagnoses. In radiology, AI tools have been developed to assist in detecting conditions like cancer or cardiovascular diseases from medical images with high accuracy, supporting radiologists in making more informed decisions. This technological advancement ensures timely and precise treatment, ultimately improving patient outcomes .

Effective risk management in healthcare is supported by data analytics, which helps identify potential risks and implement mitigation strategies. By analyzing past incidents and patient outcomes, healthcare providers can pinpoint areas of concern and develop protocols to enhance patient safety. For example, predictive analytics can identify patients at risk of adverse drug reactions, allowing for preventive measures to be taken. This proactive approach not only safeguards patient health but also reduces legal and financial risks for healthcare institutions .

Patient engagement is crucial for achieving optimal health outcomes, and data analytics plays a vital role in this process. By providing patients with access to their health data and personalized insights, they become more involved in their care decisions. Personalized medicine approach by providing the personalized and customized intervention, including education, monitoring, follow up, etc. would be the best approach for a patient centric strategy

Patient Engagement is Key. A mHealth app could leverage the data and deliver personalized experience in the following form- Targeted push notifications: Appointments, refills & preventive care. Convenient in-app chat: Facilitate non-urgent communication. Gamification: Motivate patients with rewards programs. It could be a powerful step towards empowering patients with real-time health data for proactive management.

In addition to the areas mentioned above, namely Personalizing Care, Predictive Analysis, Operational Efficiency, Enhancing Diagnostics, Risk Management, Engaging Patients, one major use of data analytics in revolutionizing Healthcare is to decrease cost of healthcare in general, using principles of Population Health Management (PHM). This benefits both the Payers as well as Providers in long run. In addition, by applying the data analytics to cohorts of population, the process of disease management becomes more efficient and cost effective in chronic diseases, hereditary diseases. Data Analytics in large population cohorts help in planning Public Health interventions, Community Health Practices and also Preventive Health practices.

Challenges to consider: Security concerns: Ensuring the security of sensitive patient data collected by IoT devices is paramount. Infrastructure investment: Implementing a network of interconnected medical devices requires significant upfront investment in sensors and data management systems. Integration with existing workflows: Integrating any new system with existing workflows and staff training on data interpretation is crucial for successful implementation.