Big data is revolutionizing industry after industry, making each one more efficient and cost-effective. Healthcare management is one of the fields experiencing the greatest benefits of using digitized, standardized, consolidated, analyzed and modeled information. Professionals can apply big data in top-down approaches to healthcare through population analysis and top-up through individual analysis. The results are promising, helping initiate system-wide cost reductions, more effective ongoing patient care and disease preventions and cures.
Healthcare management institutions are heavily investing in big data research to complement practitioners' expertise. Providers can use technology tools and algorithms that make massive accurate data collection possible. Analytics experts are converting large data collections into actionable and contextual information that informs clinical decision-making and improves patient care management. Healthcare professionals leverage data from disparate sources to provide insights and innovations that would be otherwise impossible to achieve.
Examples of Big Data in Healthcare Management
Patient care: By leveraging data on patient care, medical claims, health outcomes and a variety of other variables, medical professionals can utilize big data and their own personal experience to discern what works across the populations they serve.
Population health: Professionals can use cohort analyses regarding spending, types of care and root causes to address utilization outcomes and overall population health about specific conditions.
Imaging informatics and precision care: AI and ML systems use an algorithm that enables the machine to learn how to perform a task. These systems are becoming increasingly interconnected and even globalized, and big data will continue to inform the interpretation of complex medical images, such as MRIs. Big data can also inform radiology protocols, helping practitioners to deliver targeted dosages to cancer cells while limiting the rest of the patient's bodily exposure to radiation.
Preventative and ongoing care medicine: IoT devices like the Fitbit, Apple Watch and diabetes and blood pressure monitors track individuals' health, behaviors and movements. The devices send data to physicians so they can monitor progress and make regimen changes. Nutritionists also work with medical care providers using this data to inform the guidance they provide for health outcomes, such as reduction of cholesterol or lower blood pressure.
Preventing costly errors, waste, abuse and fraud: One of the culprits for the fast-rising costs of healthcare is inefficiency in recognizing threats. Organizations can use data analytics tools to identify the failures of the system to catch those who engage in malpractice, mismanagement or outright fraud and abuse. Significant reductions in these areas can go a long way toward controlling the costs of healthcare services and making care more widely accessible and affordable.
Pharmaceutical cost controls: Big data has already made a big impact in helping doctors and individuals find the most cost-effective pharmaceutical solutions for treating conditions and managing diseases. Now, professionals can use predictive analytics to capture targeted insights about the efficacy of drugs on specific groups to improve outcomes and reduce costs. For example, a drug used to treat a specific disease may have greater efficacy in certain populations or certain regions due to climate and environmental factors. The best solution for one group may not be the same for all groups.
Challenges with Using Big Data
Outcomes from the use of big data sets and algorithms are only as good as the information itself, which presents various challenges. Providers must consider how data is collected and accessed, whether technologies have a track record of success and the implicit and explicit human biases involved.
Technology developers work with experts and end-users to understand providers' clinical applications, allowing collaboration to solve problems. For example, in a 2019 study, researchers from the University of California Berkeley discovered racial bias in a predictive analytics platform referring high-risk patients to care management programs. Understanding such biases is the first step in preventing racial inequities in the system.
The collection itself is a massive undertaking involving new collaborations between institutions in order to glean statistically significant data sets. Data centralization, ownership and stewardship are other considerations. Where will the information be hosted? Who will have access? The answers to these questions are impacted by a variety of factors, including information security and patient privacy.
The recently enacted California Consumer Privacy Act requires businesses to inform consumers about the personal information they collect and how they will use it. More states plan to pass similar laws. This legislation says that companies cannot use consumers' personal information for any purpose they didn't disclose at collection.
This is a challenge that requires innovative solutions. Researchers from the Perelman School of Medicine at the University of Pennsylvania recently developed a plan to protect patient confidentiality. In a study published in Scientific Reports, the team described a new method that enables clinicians to train machine learning models while preserving patient data privacy.
Big data will continue to drive innovation, reform and new efficiencies in the healthcare management system needing improved cost control, as well as population and individual health outcomes. The keys to its success ultimately won't be the machines but the expertise of the people who manage them. A focused, online and widely accessible advanced business degree in healthcare management is where the revolution begins.
Three Reasons Managed Care Organizations Should Use Advanced Analytics
Top 6 Challenges Healthcare Executives Will Face in 2020
State of California Department of Justice: California Consumer Privacy Act (CCPA)
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