Data science in healthcare: use cases and benefits offered

Peeling back the layers of data science in healthcare.

Healthcare has been a data repository for quite some time. Electronic Healthcare Records (EHRs), patient summaries, clinical test results, medical imaging, and insurance claims represent just a fraction of the diverse and extensive data sources that have been generated at an unprecedented rate. As a 2023 Deloitte report highlights, the life sciences and healthcare industries account for 30% of the world’s data. The implementation of data science methods in this context serves a dual purpose: to help extract new knowledge and to transform healthcare delivery, thus creating new opportunities for better patient outcomes. In this article, we will discern the nuanced mechanisms through which data science is reshaping the healthcare industry, from increasing the rate of accurate first-time-right diagnoses to curbing the incidence of lifestyle-related diseases.

What we talk about when we talk about data science in healthcare

Data science combines multiple disciplines and techniques that allow healthcare actors to discover areas where progress can be made. While primarily anchored in methods such as big data analytics, data mining, Artificial Intelligence (AI), statistics, advanced Machine Learning, computer vision, and semantic reasoning, its scope isn’t confined to these alone. Such a wide array of data science techniques allows healthcare institutions to break down data silos and refine decision-making. When it comes to healthcare data analytics, there are four primary research perspectives available to data scientists:

• Descriptive analytics helps to scrutinize data in order to discern patterns, trends, and relationships. It’s the storyteller of the data world as it searches for answers to the fundamental question: “What happened?”. In healthcare, descriptive analytics sifts through vast volumes of health data, such as patient medical records or treatment records.
• Predictive analytics answers the question: “What is likely to happen in the future?”. Healthcare professionals utilize sophisticated algorithms and statistical models to pinpoint patterns in historical data that can be indicative of future events, such as disease outbreaks or patient readmissions.
• Prescriptive analytics recommends the best course of action to achieve a desired outcome. It responds to the question, “What should be done to achieve a specific goal?”. For example, it can recommend personalized treatment plans for patients based on their behavioral data, genetic makeup, or environment.
• Diagnostic analytics goes beyond the ‘what’ of descriptive analytics and the ‘what might happen’ of predictive analytics. Instead, it investigates the ‘why’ — uncovering the root causes of specific healthcare outcomes. Diagnostic analytics helps to answer questions like “Why did this patient’s condition worsen?” or “What factors led to the success of this treatment plan?”.

One of the main reasons data science offers a bright promise to healthcare is the unprecedented data deluge that has engulfed the healthcare sector. With the rapid digitization of healthcare systems, an immense volume of data is generated daily. EHRs, clinical trial data, health insurance claims, wearable devices, genetic and genomic data, medical imaging, and even patient-generated data from sources like mobile health applications and social media interactions contribute to this vast pool of information (see Fig 1.). According to the global investment bank RBC Capital Markets, the annual growth rate of healthcare data is set to achieve a 36% increase by 2025, outpacing other sectors such as manufacturing, financial services, as well as media and entertainment.Figure 1. A list of data sources in the healthcare industry

This data tsunami can be seen both as a challenge and an opportunity. On the one hand, researchers are often confronted with questions of how to access or gather representative data with high-quality annotations. Moreover, data often comes in an unstructured form from different sources, creating a challenge of interoperability. Privacy and security pose other concerns for those dealing with large data volumes, especially when it comes to vulnerable patient health data. These nuances are just a drop in the ocean of healthcare’s complex data science landscape.

On the other hand, the sheer volume and diversity of data offer the potential for significant discoveries and improvements in patient care, research, and healthcare systems. This wealth of data provides researchers with an extensive foundation on which to base their investigations of the human body. It allows for the exploration of advanced strategies of drug development, the identification of novel biomarkers, and the exploration of new approaches to clinical trials. Moreover, healthcare administrators and policymakers can leverage data-driven insights so as to make informed decisions about resource allocation, infrastructure development, and policy planning.

What’s more, AI, with its ability to analyze complex patterns, has become a linchpin in healthcare data analysis in recent years. Particularly adept at handling rich data types, such as genetic sequences, medical images, and electronic health records, it can process these multifaceted datasets swiftly and accurately. In a recent Nature survey of more than 1,600 researchers, the majority of respondents admit that AI offers faster ways to process data, accelerates computations, and saves an individual’s time and/or money. Meanwhile, the number of research papers mentioning AI or Machine Learning concepts in their titles or abstracts has increased up to 5% in life sciences and healthcare from 1983 to 2023.

Healthcare as a data goldmine

From propelling precision medicine to advancing public health, data science in healthcare is found in applications across the various healthcare segments and serves diverse purposes. Let’s delve into the implementations of data science tools in the industry:

Clinical care and treatment

• Predictive analytics for disease diagnosis and progression. Predictive analytics, powered by Machine Learning algorithms, aids in early disease detection and prognosis. For example, data science models can analyze patient data in order to forecast whether a patient is at high risk for a specific chronic disease. Early interventions and personalized prevention strategies, such as lifestyle modifications and medication, can be then implemented to prevent negative outcomes.
• Personalized treatment plans based on image processing. Advanced image processing facilitates personalized treatment strategies. In oncology, AI-driven image analysis can precisely outline tumor boundaries, aiding in targeted therapies and minimizing damage to healthy tissues. Similarly, in neurology, image analysis techniques help to develop treatment plans for conditions like brain tumors or strokes.
• Wearable technology analytics and medical signal analytics. Wearable devices, such as smartwatches and fitness trackers, continuously collect data on patients’ vital signs and activity levels. Data science techniques process this information and empower healthcare providers to remotely monitor patients with chronic conditions like diabetes or hypertension. Abnormalities in vital signs can trigger timely interventions or adjustments to treatment plans.
• Clinical decision support systems (CDSSs). Although their use should be further explored, Natural Language Processing (NLP) enabled CDSSs have a great potential to support decision-making in healthcare settings. In a 2023 study conducted in a Norwegian hospital trust, a system integrating unsupervised and supervised Machine Learning techniques with rule-based algorithms successfully identified critical allergies for patient safety during medical procedures. Furthermore, the system implemented a semi-supervised Machine Learning approach that automated the annotation of medical concepts within narratives. The majority of respondents expressed a positive attitude towards the system and its potential use in the future. As you may have already realized, the health care system in the modern world is not standing still at all but is constantly evolving. And thanks to fhir implementation is becoming more and more convenient and reliable to use.

Operational efficiency and healthcare management

• Workflow orchestration. Understanding the current operational landscape of a hospital is nothing short of a Herculean task. It’s the unpredictability inherent in healthcare settings, sudden surges in patient admissions, varying staff availability, and the very nature of emergencies, that create the critical need for robust data tools within healthcare administration. A proactive approach can prevent bottlenecks during peak times and minimize idle resources during lulls.

For example, healthcare providers can achieve greater operational efficiency through process mining, which implies the analysis of event logs to grasp and optimize processes. In healthcare, this translates into evaluating the sequences of activities that occur during patient care. Following the journey from admission to discharge, administrators can pinpoint delays and continuously monitor performance across various departments.

• Supply chain management. In healthcare organizations, supply chain management is crucial for the seamless flow of medical supplies, pharmaceuticals, and equipment. However, this critical function comes at a significant cost, with supply chain management expenses often accounting for one-third of the total operating expenditures. In this case, data analytics emerges as a potent instrument for optimization.

One noteworthy example of how healthcare facilities can leverage data analytics for green performance is the concept of the Big Data Analytics Capability (BDAC). Researchers emphasize that BDAC can enhance information sharing and streamline internal communication, enabling healthcare organizations to adopt more environmentally friendly practices. In addition, through precise demand forecasting and efficient logistics, BDAC can minimize unnecessary resource consumption.

Public health and epidemiology

• Contact tracing for disease prevention. Data science allows for efficient contact tracing through the analysis of vast datasets, including GPS data and mobile phone records. For healthcare businesses, the implementation of data science not only enhances operational efficiency but also ensures more personalized and preventive care, significantly improving patient outcomes. Automated contact tracing apps, supported by data algorithms, help identify potential exposures quickly. These technologies were widely used during the COVID-19 pandemic to track and contain the virus’ spread.
• Sentiment analysis for precise information dissemination. This data science technique can analyze public sentiment through social media and online platforms. Understanding public perceptions and behaviors can inform healthcare institutions on how to appropriately tailor public health messaging during disease outbreaks and promote accurate information dissemination.
• Genomic surveillance. In the case of viral outbreaks like COVID-19, genomic sequencing of virus samples provided valuable data to researchers. Health data scientists analyzed these genetic sequences to track the virus’s mutations and assess its impact on vaccine efficacy. Furthermore, genomic surveillance was essential for monitoring the evolution of the SARS-CoV-2 variants.
• Real-time data analysis for public health emergencies. With real-time data analysis, public health agencies can respond quickly to emerging health threats. Timely information about disease patterns, geographic hotspots, or population segments at risk opens up space for targeted interventions, such as accurate medical team deployment, resource distribution, or prompt implementation of quarantine measures.

The promise of better healthcare delivery

Despite being in its earliest days, data science holds the potential to become a cornerstone in the emergence of a value-based care model that places the focus squarely on patient outcomes and the efficient use of resources. Here are some of the key benefits it can bring to the healthcare industry:

• Wider healthcare accessibility at a lower cost. Healthcare providers can extend their services to underserved and remote areas. This could be one of the strategies to break down geographical barriers and make healthcare more accessible to diverse populations through data-driven decision-making.
• Greater emphasis on primary and secondary prevention. The analysis of big healthcare datasets makes it possible to identify patterns and risk factors, paving the way for more precise preventive measures. Primary prevention methods, such as lifestyle modifications and early interventions, can be tailored to specific demographics. Additionally, the early detection facilitated by data analytics allows for timely secondary prevention.
• Cost-efficiency. From more ample resource allocation to a greater rate of first-time-right diagnoses, data science tools offer a new way of looking at evidence-based medicine and healthcare management.

Today, healthcare professionals are increasingly equipped with a powerful toolset to navigate the complexities of patient care, administrative efficiency, and public health management intricacies. In the coming years, data-driven decision-making will likely extend its influence beyond clinical practices to shape healthcare policies and public health initiatives. Governments and healthcare institutions will leverage data analytics to formulate strategies for disease prevention, healthcare resource planning, and the development of innovative treatments.

From numbers to cures

This intersection of data science and healthcare is not merely a trend. It represents a fundamental shift in how we are approaching medical challenges. Data science, embedded with advanced analytics tools, Machine Learning algorithms, and AI technology, acts as a guiding force for extracting relevant insights from this wealth of information. But, although it offers a bright prospect for the future, cohesive, meaningful, and impactful digitalization is still in its early stages. The application of new instruments requires a gradual transition towards digital healthcare systems where the patient takes center stage. This may be the way for us to unlock the full potential of data, ushering in a phase where the healthcare industry represents not only reactive and curative treatment, but stands as a truly personalized, preventive, and participatory experience.

Avenga helps healthcare companies discover actionable data-driven insights at the right time with the right toolkit. Learn more about our expertise in the domain: contact us.