Machine Learning (ML) is gearing up to make a real impact on healthcare. The application of ML in hospitals and clinics is projected to grow by 10.6%, offering significant benefits, where ML can assist healthcare professionals in diagnosing diseases, identifying anomalies, saving time, and facilitating personalized treatments. Furthermore, it has the potential to accelerate and reduce the cost of drug discovery, a long-anticipated benefit for data scientists in healthcare.
A recent McKinsey study suggests that big data analytics could save the US healthcare sector approximately $300 billion annually, equating to about $920 per person. This saving, which represents 8% of the sector’s total expenditure, results from improved diagnostics, fraud reduction, and enhanced patient outcomes. Therefore, ML is not merely a buzzword, but a practice set to significantly improve patient outcomes and overall care.
Consider a prevalent challenge in healthcare management: staffing. Determining the required number of staff at any given time is crucial. Overstaffing leads to unnecessary labor costs, while understaffing can compromise patient care. Big Data and healthcare analytics are stepping in to address this issue.
For instance, several hospitals in Paris, part of the Assistance Publique-Hôpitaux de Paris, are utilizing Big Data to predict patient numbers as outlined in an Intel whitepaper. They analyze various data sources to forecast daily and hourly patient counts at each facility. A decade’s worth of hospital admissions records forms a crucial part of their data. Data scientists employ time series analysis techniques to identify patterns in these records. Then, ML is used to find the most accurate algorithms for predicting future admissions. This approach optimizes staffing levels, enhancing efficiency and patient care.
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This trend has extended to the pharmaceutical industry, promising to enhance the value of medicine and prescribing quality. Express Scripts, a third-party organization managing medicine coverage for health insurance providers, exemplifies this application of Big Data in healthcare. The US-based company has collected data from 83 million patients, encompassing clinical and behavioral characteristics and even social media interactions. This data enables Express Scripts to predict critical scenarios, such as potential addiction to a medication or non-adherence to treatment. In such cases, the company provides personalized interventions and support, resulting in a reduction of the non-adherence rate among hepatitis C patients from 8.3% to 4.8%.
While the potential of Large Language Models (LLMs) in healthcare is exciting, we must consider the practicalities and costs associated with realizing the gains it promises. The medical data we’re dealing with is diverse, coming from various platforms like EHRs, medical images, and IoMT devices. Each has its own format, adding to the complexity of data management.
While EHRs offer immense potential, their full implementation is a challenge that many countries continue to face. The U.S. has made substantial progress, with 94% of hospitals adopting EHRs as per HITECH research. The EU is still catching up, but with a strong directive from the European Commission, this is expected to change. Medical imaging, a critical part of healthcare with around 600 million procedures performed annually in the US, adds to this complexity.
With new Big Data tools and practices emerging, staying current is a challenge. While the promise of medical data is exciting, it’s clear that managing and analyzing it is a complex endeavor. We must factor in the costs of managing, storing, and delivering this data. If we don’t, our expectations of cost savings and improved patient outcomes may be hindered.
In conclusion, ML and Big Data hold immense potential in healthcare, but realizing this potential requires careful consideration of the associated challenges and costs.
Dr. Ofer Markman
is Filo Systems' Co-founder and VP Business Development
