Healthcare Big Data and the Promise of Value-Based Care
Big Data is essential to every significant healthcare undertaking. Read about the challenges, applications, and potential brilliant future for healthcare big data.
What Is Big Data In Healthcare?
“Big data” refers to the extensive collection of health data from numerous sources, including electronic records, medical imagery, genomic sequence, payor records, pharmaceutical research, wearables, and, to name but a few. It is distinguished by three characteristics from the traditional electro-health data used in decision-making: It is available in exceptionally high volumes, moves at high speed, spans the massive digital world of the health industry, and is extremely variable in structure and nature as it comes from many sources. The 3Vs of Big Data are known.
It is difficult to combine large health data into conventional databases, making it extremely difficult for industry leaders to process and make use of their important promises of industry transformation, thanks to its diversity in form and type, and context.
Despite these challenges, several new technological improvements are allowing healthcare big data to be converted to useful, actionable information.
Despite these challenges, several new technological improvements allow big data to be transformed into useful, operational information. Big data informs the movement towards value-added healthcare by using appropriate software tools and is opening the door to remarkable progress even while reducing costs. With the rich information provided by health data analytics, carers and administrators can better decide on medical and financial matters whilst continuing to provide a constantly increased quality of patient care.
However, big data analyzes have been lagging behind other industries as a result of challenges such as health information privacy, safety, data siloing, and budget limitations. Meanwhile, 80 percent of managements from financial departments, insurance companies, media, entertainment companies, manufacturing companies, and logistics companies surveyed report “successful” their investment in the processing of the big data.
At least two trends encourage the healthcare sector to use big data today. The first is the move to a value-based care model that rewards them based on their patient populations from an income-for-service model, which financially rewards caregivers for performing procedures. Health data analysis will allow population health to be measured and tracked, thus enabling this switch. The second trend involves providing evidence-based information using Big Data analysis to increase efficiencies over time and to improve our understanding of best practices in connection with all diseases, injuries, or illnesses.
Using big data analysis to deliver information that is evidence-based will, over time, increase efficiencies and help sharpen our understanding of the best practices associated with any disease, injury or illness.
With the use of big data in healthcare, the industry will undoubtedly be able to transform from a service fee model to value-based care. In brief, it can fulfill the promise to reduce medical care costs and reveal ways of delivering superior experiences, treatments, and results for the patient.
Applications for Big Data in Healthcare
Patients are at the forefront of any priority list, keeping patients healthy and preventing disease. Consumer products such as the activity tracker Fitbit and the Apple Watch keep tabs on the levels of individual physical activity and can report certain trends in health. The results are already forwarded to cloud servers and provide information to doctors who use it in their overall health and wellness programs.
Fitbit has already worked together with United Healthcare to reward its insureds for regular exercises up to $1500 per year. The one-drop application of Informed Data Systems for Android and Apple is making major changes to A1c for diabetes sufferers. In the meantime, the HealthKit, CareKit, and Researchkit of Apple are using technology incorporated into the mobile devices of Apple to help patients manage their conditions and to help researchers gather data from hundreds of millions of customers around the globe.
Expanded diagnostic service provides more access to care for patients. Mobile device apps such as Aetna’s Triage inform patients using aggregated data on their medical condition and can recommend medical care to patients based on the app inputs.
- Another of its health data initiatives, which is a condition of approximately 130.000 Americans annually, has been worked together by Apple with scientists at Stanford to determine whether Apple Watch’s heart sensor can be used for the detection of atrial fibrillation. Apple can inform the wearers that they need to seek medical care if the device is successful in detecting disease.
- Propeller Health uses a Bluetooth-enabled sensor for people with asthma or COPD that fits inhalers and spirometers. To better understand the cause of their symptoms and to take measures to prevent attack the company tracks the environment at the sensor locations and sends reports to patients’ phones. The company also sends reminders when medication is to be received. Propeller reports that patients have experienced a 79 percent lower number of asthma attacks and have 50% more symptom-free days when 34 peers have been reviewed to date.
- Reducing errors in prescription enhances results and save lives. Recorder errors cost around 21 billion dollars annually, affecting over 7 million U.S. patients and resulting in 7 000 deaths, according to the Network of Excellence in Health Innovation. The Israeli startup MedAware partners healthcare organizations, using large data to identify prescription errors before they happen, to deploy its decision support tool.
Reducing costs. The increased insight into medical data translates into improved treatment, shorter hospitalizations, and fewer admissions and re-admissions for the physicians.
- To identify patients with more than one chronic condition (comorbidity), the Mayo Clinic uses big data analytics to benefit from early care interventions, saving them from visits to the emergency department.
- Knowledge from the big-data analysis provides clinical insights that do not otherwise exist for healthcare providers. They can prescribe treatments and take clinical decisions more precisely and eliminate the hypotheses that frequently involve treatment, which leads to lower costs and improved patient care.
- Analysis of large-scale healthcare data also helps gain insight into patient cohorts that are at greater risk of disease, allowing a proactive approach to prevention. In short, the analysis of large-scale healthcare data can identify patients outside of the standard who use healthcare. It can identify protocols or procedures for which the results are inferior or whose costs are excessive. It can be used for education, information, and motivation of patients for their own well-being. It can highlight the efficiency and effectiveness of treatment plans by bringing financial and clinical data into play.
Healthcare Big Data Lakes Become “Oceans”
Just like a researcher prefers to work with, say, millions of values rather than hundreds of sample sizes, the more information a big sample gives, the better. Although the term “data lake” is often used to describe a collection of raw big data, there are several events that promise to create research and analysis opportunities for what may be referred to as “data oceans.”
The benefit of integrating and sharing data in clinical research to supplement such “oceans” is recognized by researchers and funding agencies. For instance, the University of Oxford Li Ka Shing Center for Health Information & Discovery offers UK Biobank access and plans to add 50 million electronic records for patients. Moreover:
- The European Medical Information Framework (EMIF), as well as cohort datasets from participating research communities, is meant to improve access to health data from electronic health records for some 50 million Europeans.
- Open PHACTS is a platform for researchers and others in need of access. It has been developed in cooperation with academic and business organizations, which enables users to obtain information and make decisions on complex pharmacological issues.
- More than 15 petabytes of data derived from 390 million medical records, patient inputs, and imaging studies have been aggregated in the Dutch Multi-National company N.V. Philips. Critical data on clinical decision-making can be obtained from healthcare personnel in this massive collection.
- In the USA, the National Institute of Health has established a program to bring biomedical big data to scientists, clinicians, and other scientists. Big data to knowledge (BD2K). These initiatives will increase the capacity of healthcare providers to improve patient care while at the same time addressing the unsustainable cost path. It also offers a rich world of accessible and disease prevention and cures data and information to researchers.
Challenges for Implementing Big Data in Healthcare
Healthcare organizations face challenges with healthcare data that fall into several major categories including data aggregation, policy and process, and management. Let’s explore these further.
Challenges for data aggregation: Firstly, many payors, hospitals, administrative offices, government agencies, servers, and file offices are often given information on patients and their financial situation. It requires planning to be put together and arranged to work with all data producers in the future, as new data is produced. Each participating organization must also understand and agree on the types and formats of large data that they wish to analyze. To ensure that the correctness and quality of such data are not only problems with the format in which it is stored (paper, film, traditional data databases, EHR, etc.). This requires not only data cleansing (usually a largely manual process), but also a review of data governance: Was the data recorded accurately, or have errors crept in, perhaps over a period of many years?
Policy and Process Challenges: Various process and policy issues should be addressed once data have been validated and aggregated. The HIPAA regulations require health information to be protected by policies and procedures. The task is complicated by access control, authentication, transmission security, and other rules. To some extent, cloud service providers have solved this multifaceted issue, perhaps most notably the Amazon AWS, which offers HIPAA and PHI complying cloud services (PHI).
Challenges of management: Lastly, to realize the promises of big data analysis in healthcare, organizations must adapt their business practices. Data scientists and IT staff with necessary analytical skills are likely to be required. Certain organizations, although cloud services providers mitigate some of those concerns, may have to “go to and replace” much of their IT infrastructure. Doctors and administrators can take time to rely on the previously unseen advice of Big Data.
The Brilliant Future for Big Data in Healthcare
The prospect for healthcare becomes ever more exciting as managers in the business and industry declare their Big Data initiatives successful and transforming. Below are a few areas in which large data are intended for health care transformation.
Precision medicine is intended to enroll one million people in the All Us research program to volunteer their health information. The NIH Precision Medicine Initiative includes the program. According to the NIH, this initiative aims “to understand how a person can help determine the best approach to prevent or treat diseases in his or her genetics, environment and lifestyle. The Precision Medicine Initiative’s long-term objectives are to bring precision medicine to all health and healthcare sectors on a broad scale.”
Wearables and IoT sensors, which have already been mentioned, are capable of revolutionizing many patient populations’ health and helping people stay healthy. A wearable device or sensor is enabling medical staff to monitor and then consult the patient face-to-face or remotely for direct, real-time feeds to the electronic records of a patient.
Machine learning, an artificial intelligence element, and a data-dependent component, already help doctors improve patient care. IBM already has partnerships with the Mayo Clinic, the CVS Health, the Sloan Kettering Cancer Centre, and others with its Watson Health computer system. In combination with big data analytics, machine learning increases the capacity of caregivers to improve care for them.
Fueling the Big Data Healthcare Revolution
Big data have only just begun to revolutionize healthcare and advance the industry in many areas. The changes in the field of healthcare medicine, technology, and financing, promise to improve patient care and increase health care value. However, stakeholders—providers, contributors, pharmaceutical manufacturers, government and policy-makers, and scientific and research groups—are required to work together and innovate to reinvent their system design and performance. They need to construct technology infrastructure and converge a large volume of healthcare data, an estimated 2.314 exabytes by 2020 by industry analysts. To guide us towards this new and thrilling limit of human health and well-being, they must also investment in human capital – IT specialists, data scientists, data architects, and Big Data engineers.