Over the last few years, our reliance on data analytical techniques has increased profoundly. The use of data science led to cost reduction and improved operational efficiency across government agencies and private corporations. Thus, it is not surprising that the healthcare sector gained significantly from its application.
Given that 1.2 billion medical documents are produced yearly in the US alone, there is massive scope for data science adoption to improve diagnosis, treatment effectiveness and patient satisfaction, while simultaneously trimming operational expenditure.
Below, we highlight six of the most pertinent uses of data science use cases in the healthcare sector.
Patient Data Management
In order to manage patient data effectively, machine learning led to the creation of Electronic Health Record (EHR) systems wherein patient data is systematically collected in a digital format. All paperwork is now done electronically, storing the entire medical history of the person in a single system. This widespread application of big data allows for massive overhead reductions and improvements in operational efficiency. In a cost-benefit study of EHR systems in primary care, it was estimated that net benefit of using an electronic medical record was $86,400 per provider for a 5-year period.
Furthermore, in addition to its significant benefit for health care providers, EHR allows for an improvement in health care quality, enhancing care coordination between health settings. This has a significant long-lasting impact on patients’ health outcomes. One such example is found in McKinsey’s Industry Report, which states that this integrated system “improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.”
Real time Alerts
It is pertinent to regularly monitor patient’s health statistics to ensure the quality of care, especially as we continue to navigate the pandemic and its resulting ‘virtual-first’ care movement. Therefore, with adoption of wearable devices for the purposes of remote patient monitoring, health data is collected continuously and stored on the cloud for medical purposes. A health intelligence platform, such as SenSights.ai, then gathers this data to create patient profiles and provide 24/7 insights. Data science allows health practitioners to remotely monitor a patient’s vital statistics, set condition or treatment specific ranges and provide real-time medical assistance, as necessary. If a patient’s pulse rate drops alarmingly, for instance, these tools can issue real-time emergency alerts to care providers who can then assess and treat the patient with the necessary care.
Safe@Home is one such example of a remote patient monitoring solution that uses our SenSights.ai platform to help track and manage the progression of early cognitive decline amongst older adults. It uses real-time vitals monitoring, wandering and fall detection and smart medical alerts to empower seniors’ aging in place.
Staffing Process Advancement
A classic use of Data Science in healthcare is to ensure that the correct number of staff is available in-house at any given time. Staffing decisions have an integral impact on cost and quality. Staffing shortages, for example, may compromise quality of healthcare. A staffing surplus, on the other end, results in higher costs. Therefore, predictive analysis is an important tool that allows staffing managers to determine daily as well as hourly patient arrivals at hospitals or clinics. This leads to a streamlined staff management process and patient waiting time reduction, while simultaneously improving the quality of care.
Another important use case of data analytics in healthcare is infection prevention and control (IPAC) for frontline workers. Now, more than ever, the COVID-19 pandemic highlights the need for infection screening and risk assessment tools to ensure the health and safety of healthcare staff and patients. Safe2Work, developed by Markitech, is one such data analytics tool that tracks and analyzes key biometrics – including heart rate, heart rate variability and oxygen saturation and temperature – to create an infection risk profile. Healthcare entities may then use these risk profiles to make staffing decisions that lower the onward risk of infection.
Data Security Enhancement
Given the increasingly data-intensive nature of the healthcare sector, it is highly prone to cyber-attacks that target patients’ personal data – e.g. Social Insurance Number (SIN) and Electronic Medical Records (EMRs). Therefore, it is crucial for healthcare organizations to employ predictive analytics and artificial intelligence to improve cyber security.
Such analytical tools allow organizations to closely monitor data utilization, access and sharing patterns, raising alarms in case of a network security breach. Early adoption of such sophisticated algorithmic defenses is likely to outpace the average hacker’s capabilities, minimizing the risk of data breaches.
Supply Chain Management
Over the past few years, healthcare spending increased significantly across most countries around the globe. Canada, for instance, spent a significant 10.79% of its GDP on healthcare alone in 2018, with supply chain being the largest cost-center.
Hence, it is of utmost importance for healthcare organizations to use Big Data to trim unnecessary costs and improve operational efficacy. Predictive algorithms aim to reduce variability and allow streamlining of ordering patterns and supply utilization. Though only 17% of hospital used data-driven supply chain management techniques in 2017, its adoption is hospital executives’ top priority, according to Global Health Exchange.
Patient Engagement Improvement
Given the recent shift to a ‘value-based’ care approach across healthcare providers and organizations, improving patient engagement is a top priority. Data science allows healthcare providers and payers – e.g. insurance companies – alike to predict patient behaviors. This, in turn, assists providers in developing effective communication strategies to bolster their patient relationships. It also allows them to empower patients to better manage their own health and clinical responsibilities, which results in better health outcomes and, by extension, a more positive health experience.
SenSights.ai, recently developed and launched by Markitech, is a digital health intelligence platform that employs data analytics to create detailed health profiles – aka ‘digital twins’ – for patients. This allows primary caregivers and/or healthcare providers to tailor smart monitoring solutions and communications for each individual patient resulting in greater patient satisfaction and retention.
Big Data has enormous scope in the healthcare sector from revolutionizing treatment techniques to overhauling the entire medical supply chain. It has, undoubtedly, transformed the way physicians, patients and other healthcare entities view the continuum of care. The use cases mentioned above are just the tip of the iceberg. With spending on data science compounding, we are likely to see a wider application in all areas of the healthcare industry in 2021 and beyond.
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Contributors: Mariam Javed, Arfa Amir.