How Big Data Can Improve Population Health Management


Managing population health 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 rising 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 their adoption in industry.

Big data tools like machine learning, predictive analytics, and AI can be used to mine 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 over $34 billion by 2022, growing at over 22% per year through 2022.

Powerful Use Cases

Medical imaging company Carestream has shown how big data analytics can transform the way doctors interpret all types of medical images. By using advanced algorithms to analyze countless thousands of images, a range of patterns can be identified which helps support staff provide an accurate diagnosis to their patients. As the algorithms are able to learn by reading and analyzing more images, they will constantly keep abreast of how conditions are shaping up.

The preventive element of Big Data solutions used in healthcare is very promising. The phrase “prevention is better than cure” is a perennial novelty. But using big data solutions can be a robust tool for clinicians to find indications of future health issues and communicate those signs to patients who will have the ability to make an informed choice about how to deal with those risks.

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

“We have demonstrated that we can use data from electronic health records in combination with other tools to accurately identify people at high risk of attempted suicide or death by suicide,” said one of the authors of the study, Gregory E. Simon, a Kaiser Permanente Washington psychiatrist and senior researcher 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 risk behaviors and inform treatment. For example, if an at-risk patient misses scheduled appointments, medical staff can reach out and potentially avert a mental health crisis.

Ethical considerations

As 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 the processing of data, patient trust will decline.

In a healthcare ecosystem with many different operators, all with their own unique policies on data security, close collaboration will be required to establish guidelines that enable secure sharing of data for big data purposes. Although the benefits of big data in public health are clear, they will not replace the expertise of medical personnel, but rather augment their treatment plans.

Physicians can draw on their extensive experiences treating patients and use that context to inform their understanding of insights from big data. 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 white people in England, but 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 statistical, medical and social science experts who must resolve racial gaps in deaths from COVID-19. 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 those findings.

Balancing individual rights with the “common good” is a problematic undertaking, with problems that should arise when finding 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 unlocking valuable insights from patient data could revolutionize treatment.


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