How Big Data Can Improve Population Health Management

Managing the health of the population has always been a difficult undertaking, but since the emergence of COVID early last year, this task has become even more complex. From an aging population to increasing patient expectations, healthcare providers around the world face a range of challenges.

In an effort to improve patient outcomes, reduce costs, and improve efficiency, many healthcare systems are expanding their use of big data solutions. While other industries such as insurance, banking and manufacturing have taken the lead in implementing cutting-edge big data analytics, the success of these technologies in healthcare is driving adoption. in industry.

Big data tools like machine learning, predictive analytics, and AI can be used to extract patient data to find patterns that a single doctor wouldn’t be able to see. According to a 2019 report, global big data in the healthcare market is expected to total more than $ 34 billion by 2022, growing at more than 22% per year until 2022.

Powerful use cases

Medical imaging company Carestream has shown how big data analytics has the power to transform the way physicians interpret all types of medical images. By using advanced algorithms to analyze countless thousands of images, it is possible to identify a range of patterns that help staff provide an accurate diagnosis to their patients. As algorithms are able to learn by reading and analyzing more images, they constantly keep abreast of how conditions present themselves.

The preventive element of Big Data solutions used in the health sector is very promising. The phrase ‘prevention is better than cure’ is new. But using big data solutions can be a robust tool for clinicians to find indications of future health problems and communicate those signs to patients who will have the ability to make an informed choice about how to manage those risks.

With the World Health Organization (WHO) finding that 700,000 people die from suicide worldwide each year, providing the right level of support to at-risk members of the public is critical to reducing the number of deaths from suicide. this cause. Predicting which members of the community are most likely to attempt suicide is a difficult decision for a doctor or clinician to make. But one study found that when electronic health records are combined with the results of standardized depression questionnaires, new models are able to predict suicide risk more accurately than ever.

“We have shown that we can use data from electronic health records in combination with other tools to accurately identify people at high risk for attempted suicide or death by suicide,” said one of the authors of the study, Gregory E. Simon, Kaiser Permanente psychiatrist in Washington and principal investigator at the Kaiser Permanente Washington Health Research Institute.

Linking questionnaire responses to information about previous mental health diagnoses and psychiatric medications dispensed can provide unparalleled insight into risky behaviors and inform treatment. For example, if an at-risk patient misses scheduled appointments, medical staff can reach out and potentially prevent a mental health crisis.

Ethical considerations

As more and more hospital networks upgrade their IT systems and implement big data tools to query patient data, concerns about patient privacy are likely to grow. Protecting patient data and privacy is of the utmost importance to healthcare providers, because without clearly defined rules guiding how data is handled, patient trust will decline.

In a healthcare ecosystem made up of many different operators, all with their own data security policies, close collaboration will be required to establish guidelines for the secure sharing of data for big data purposes. While the benefits of big data in public health are clear, it will not replace the expertise of medical staff, but rather increase their treatment plans.

Physicians can draw on their vast experiences of treating patients and use this context to inform their understanding of big data insights. As researchers are able to undertake more analyzes of COVID and its impact on different populations, big data can also play a key role in this work.

For example, the medical journal, The Lancet, found that after adjusting for other factors, not only are South Asian, black and mixed ethnic groups all more likely to test positive for COVID than whites in England. , they were also more likely to die from this disease.

Understanding the disproportionate impact COVID has had on these communities is not a one-step process, with an array of experts in statistics, medicine, and the social sciences set to determine the racial differences in deaths due to them. to COVID. Deploying big data tools can allow researchers to identify patterns in data on people who have died from COVID and create more effective treatment plans based on these findings.

Balancing individual rights with the “common good” is a problematic endeavor, and problems should arise when seeking that common ground. If public health experts are able to effectively communicate the benefits that all communities can derive from the widespread use of big data, then extracting valuable information from patient data could revolutionize treatment.


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