If an apple a day keeps the doctor away, healthcare data analytics will find out why, and how much to charge for the apple. According to Grand View Research, the worldwide $25.9 billion healthcare data analytics market is expected to grow 7.5% annually between 2020 and 2027.
The reason for this growth? The promise of data analytics lies in improving patient outcomes, preventing health crises, and reducing expenditures, all the while transitioning the industry from a fee-for-service care model to value-based care reimbursements.
What Is Healthcare Data Analytics?
Healthcare data analytics makes use of vast amounts of health-related data from hundreds of sources. It identifies healthcare issues and trends, supports clinical decisions, and helps manage administrative, scheduling, billing, and other tasks. At the same time, it protects patient privacy and ensures data security against hacks and breaches.
Healthcare data analytics technology and tools include the following:
Artificial Intelligence (AI)
In the healthcare industry, artificial intelligence is used for everything from administrative tasks to drug research. AI technology is designed to handle vast quantities of data, and uses algorithms, natural language processing, and machine learning to get better at what it does. According to ABI Research, healthcare AI spending should grow to $2 billion between 2019 and 2024.
Healthcare business intelligence tools gather patient medical data that includes physician visits and diagnoses, prescriptions, and payments and billing for use in analyzing the effectiveness of care, patient outcomes, financial activities, and more. They help practices and hospitals understand the state of their operations, allowing them to identify trends and make data-based decisions.
According to Research and Markets, the global healthcare business intelligence market is expected to grow over 17% between 2019 and 2026, to nearly $16 billion.
Blockchain technology is key to data security in healthcare. U.S. patient data is protected under the Health Insurance Portability and Accountability Act. Blockchain technology uses cryptography to secure data and protect patient privacy.
The promise of blockchain is that it can give healthcare providers and researchers access to massive amounts of data while also protecting patient information, leading to better patient outcomes, larger datasets for improving AI accuracy, and potentially faster drug development.
What Is Healthcare Data?
Healthcare data includes everything from one patient’s demographic information and physical address to the results of clinical research trials with hundreds of subjects. The volume of healthcare data is expected to increase 36% through 2025, according to an International Data Corporation report. Where does it all come from? By one estimate, a single patient generates over 80MB of data each year, according to Frontiers in ICT.
Sources of healthcare data include:
- Electronic health records (EHRs): Patient records that include location, demographics, and medical history
- Clinical data: Data compiled from diagnoses and outcomes, medical prescriptions, clinical trials, and the like
- Administrative data: Data from billing, payments, scheduling, coding, insurance reimbursements, and more
- Imaging data: Mammograms, MRIs, X-rays, and other scans
- Wearables: Data collected from consumer health products such as Fitbits and from prescribed technology such as mobile heart monitors
What Drives the Growth in Healthcare Data?
The convergence of technology and public policy are the main drivers of healthcare data growth. When the Affordable Care Act was enacted in 2010, it called for patient EHRs to replace paper files.
Clinical technology, such as digital mammograms and other scans, produce still more data, as do wearable tech devices. Analytical tools that provide insights and office administrative tools also drive the use of healthcare data.
Finally, the transition from a fee-for-service model to a value-based care model is also propelling the growth of healthcare data.
What is the Role of Data Analytics in Healthcare?
Data analytics tools and technology, such as EHRs, chatbots, and data dashboards, are an integral part of the healthcare data analytics landscape. They streamline data gathering and patient scheduling. They provide patient information in context rather than just as a history of medical codes. And they help healthcare administrators make forecasts based on actionable data.
From the first moment that a patient contacts a provider through the billing and payment stages, healthcare data collection is underway. Automated systems collect data to identify, schedule, diagnose, treat, prescribe for, and follow up with patients.
Using data visualization tools such as healthcare dashboards, healthcare professionals can see trends in general health; identify efficiencies in a practice; get assistance with staffing, scheduling, and supply ordering; and understand the financial health of a practice or hospital. They also can automate decisions based on the data; for example, they can set up a process to automatically send out appointment reminders or reallocate resources if a trend shows a flu outbreak.
At a population or clinical trial level, researchers may use healthcare data analytics to understand the health trends of a community or use artificial intelligence to analyze vast quantities of clinical trial data. They can combine healthcare data with social determinants such as employment status, education level, and environmental impacts to identify healthcare issues within a community and establish preventive care initiatives to improve general health.
The following are some of the ways that the industry uses healthcare data analytics.
Improved Scheduling and Staffing Procedures
According to a study by SCI Solutions (now R1 RCM), missed appointments cost the healthcare industry $150 billion in 2017, the latest year for which there is data. Predictive modeling can help providers understand who is most likely to be a no-show at their next appointment.
AI-based chatbots walk patients through the scheduling process, collect insurance data and symptoms, and can also remind patients of prescriptions, refills, and follow-up appointments, letting staff focus on higher-order tasks.
Streamlined Administrative Processes
Healthcare administration is highly complex. Administrators can use healthcare data analytics to forecast demand and schedule staff and physicians, automate the ordering of supplies based on past usage trends, and expedite billing. The more smoothly a hospital or clinical office runs, the better the patient experience. A hospital data dashboard provides administrators with an at-a-glance overview of patient and admission data, quality metrics, and costs.
Reduced Medical Errors and Improved Patient Outcomes
Surgical complications, pressure ulcers, diagnosis errors, and hospital-acquired infections are some of the adverse events that can befall patients. AI has shown either effectiveness or promise in reducing or eliminating these bad outcomes.
AI has been beneficial in early diagnosis of lung cancer and has shown reduced errors in reading digital images. A study published in npj Digital Medicine showed that AI was 92% effective in identifying which patients posed a bleeding risk after pulmonary bypass surgery.
AI-Informed Physician Diagnoses and Treatment
One of the most promising areas of healthcare data analytics is the ability to enhance physicians’ medical diagnoses. It’s impossible for a human being to parse the vast amount of healthcare data available, but that’s what AI is built for. Doctors still make diagnoses, but they now have research data, population health data, and EHRs to augment their clinical decisions.
On the other hand, AI has gotten better at making diagnoses without physician input. Healthcare AI uses machine learning to improve its diagnostic capabilities. The more AI is exposed to healthcare data, the more accurate it gets at identifying breast cancer or chronic conditions, such as prediabetes. AI often can successfully diagnose diseases in their early stages, making treatment more cost-effective and improving patient outcomes.
The U.S. healthcare system has long operated on a fee-for-service model — physicians perform a service, and health insurance reimburses the provider for that care. However, many experts have argued that fee-for-service incentivizes the wrong approach to healthcare, rewarding expensive interventions and treatments rather than the care that will keep patients healthy.
Value-based care takes a preventive approach, focusing on lifestyle changes that improve overall health. It may be key to developing best practices and reducing costs. A study by EHR technology provider Geneia showed that 68% of physicians surveyed agreed that analytics are key to being reimbursed for value-based care.
Population Health Management
Population health management looks at the health outcomes of a specific group of people, such as residents of a city or neighborhood, or the population serviced by a hospital network. Without healthcare data analytics, population health management would not be possible, as this data combines patient health metrics, community health metrics, and social determinants of health such as employment, poverty level, environmental exposures, education, and culture.
The growing role of data analytics in healthcare has profoundly changed the doctor-patient relationship. In some ways it has caused additional stressors; according to the Geneia survey, 86% of physicians agreed that EHR and data reporting has “diminished their joy” in practicing medicine.
Additionally, patients were reluctant to accept an AI diagnosis even when the accuracy rate was the same as or higher than that of a physician diagnosis, according to a study by the Harvard Business Review. However, the promise of improved outcomes and lower costs continues to drive the growth of healthcare data analytics.
The Future of Healthcare Data Analytics
U.S. healthcare expenditures far outpace that of any other developed nation, according to the Organisation for Economic Co-operation and Development (OECD). However, life expectancy in the U.S. has fallen, and the nation’s rates of chronic disease such as diabetes is higher than that of other OECD nations.
The goal of healthcare data analytics is to reduce expenditures while improving quality of care, as measured by patients’ general health. AI and big data, business intelligence, blockchain technology — all of these tools are aimed at improving patient outcomes. While in some areas, such as physician stress, there’s still a long way to go, the results have already shown gains, such as in improved AI diagnostic capabilities. As the data shows, the prognosis