In the Gulf Cooperation Council, healthcare organizations have complied with or implemented international standards for health privacy policy and procedures in hospitals nation-wide. Big data analytics in healthcare comes with many challenges, including security, visualization, and a number of data integrity concerns. Challenges of Big Data Analytics for Healthcare. If different components of a dataset are held in multiple walled-off systems or in different formats, it may not be possible to generate a complete portrait of an organization’s status or an individual patient’s health. Healthcare organizations face several challenges including security, data integrity, and visualization. Other information, such a home address or marital status, might only change a few times during an individual’s entire lifetime. Data interoperability is a perennial concern for organizations of all types, sizes, and positions along the data maturity spectrum. June 12, 2017 - Big data analytics is turning out to be one of the toughest undertakings in recent memory for the healthcare industry. Big Healthcare Data Analytics: Challenges and Applications Chonho Lee leech@cmc.osaka-u.ac.jp3, Zhaojing Luo zhaojing@comp.nus.edu.sg1, Kee Yuan Ngiam kee yuan ngiam@nuhs.edu.sg1,2, Meihui Zhang meihui zhang@sutd.edu.sg4, Kaiping Zheng kaiping@comp.nus.edu.sg1, Gang Chen cg@zju.edu.cn5, Beng Chin Ooi ooibc@comp.nus.edu.sg1, and Wei Luen James Yip james … Whether we approve or not, the smartwatches we wear, social media platforms we use, smartphones we carry, and genetic data we bear are slowly but surely painting the future of the healthcare we receive. Organizations should also ensure that they are not creating unnecessary duplicate records when attempting an update to a single element, which may make it difficult for clinicians to access necessary information for patient decision-making. Not only is data analytics coming up with the latest technologies to be leveraged by medical practitioners but it is also helping in taking right medical decisions regarding the treatment of the patients. READ MORE: Understanding the Many V’s of Healthcare Big Data Analytics. Thanks for subscribing to our newsletter. Overcoming these challenges will depend on whether these sources are making a substantial difference in clinical decision-making. Challenges to a Prevalent use of Big Data Analytics in Healthcare. In the analysis phase, the challenges were classified into 10 categories for further examination. These tools are likely to become increasingly sophisticated and precise as machine learning techniques continue their rapid advance, reducing the time and expense required to ensure high levels of accuracy and integrity in healthcare data warehouses. By its very nature, big data is complex and unwieldy, requiring provider organizations to take a close look at their approaches to collecting, storing, analyzing, and presenting their data to staff members, business partners, and patients. When developing hybrid infrastructure, however, providers should be careful to ensure that disparate systems are able to communicate and share data with other segments of the organization when necessary. Robust metadata and strong stewardship protocols also make it easier for organizations to query their data and get the answers that they are expecting. All data comes from somewhere, but unfortunately for many healthcare providers, it doesn’t always come from somewhere with impeccable data governance habits. The HIPAA Security Rule includes a long list of technical safeguards for organizations storing protected health information (PHI), including transmission security, authentication protocols, and controls over access, integrity, and auditing. Undeniably, big data analytics in the field of healthcare enables analysis of massive datasets from a large number of patients, recognizing clusters and relationship between datasets. Improved patient support and cost-saving mechanisms for the healthcare industry. Emerging tools and strategies such as FHIR and public APIs, as well as partnerships like CommonWell and Carequality, are making it easier for developers to share data easily and securely. Healthcare data is driven by certain protocols and conventions. Front-line clinicians rarely think about where their data is being stored, but it’s a critical cost, security, and performance issue for the IT department. Providers who have barely come to grips with putting data into their electronic health records (EHR) […] But adoption of these methodologies has not yet hit the tipping point, leaving many organizations cut off from the possibilities inherent in the seamless sharing of patient data. A non-traditional approach is likely to sit well with the penetration of technology in all aspects of our lives, but it leaves us with very complex questions. Removing data from such repositories is a huge challenge. But how successful is this trend in delivering on its hopeful promises? All rights reserved. Whatever changes ultimately take place, one thing is certain — the healthcare industry needs to adapt in time. The industry is currently working hard to improve the sharing of data across technical and organizational barriers. Finally the paper ends with the notable applications and challenges in adoption of big data analytics in healthcare. Poor data at the outset will produce suspect reports at the end of the process, which can be detrimental for clinicians who are trying to use the information to treat patients. How ethical are these data collection methods?  Are healthcare professionals equipped to employ such data-gathering means? Insurers have their own incentives which center on costs which means that they do not function as … Combining these dissimilar sources of data to understand patients at the individual level is an undeniable reality, but it will take much time and resources for a cultural, technological, and educational change to occur and for individualized personal healthcare to become standard practice within the healthcare environment. Organizations should be very clear about how they plan to use their reports to ensure that database administrators can generate the information they actually need. Challenges in healthcare data BI offers immense opportunities to improve patient outcomes, deliver precision medicine, minimize costs, reduce hospital readmissions, maximize revenue, ensure patient safety and abide regulations. The cloud offers nimble disaster recovery, lower up-front costs, and easier expansion – although organizations must be extremely careful about choosing partners that understand the importance of HIPAA and other healthcare-specific compliance and security issues. However, especially in the case of a healthcare system, this data analysis is quite complex. Consent and dismiss this banner by clicking agree. Consent, data exchange, and accuracy are further complicated by the unreliability of current patient matching technologies. From phishing attacks to malware to laptops accidentally left in a cab, healthcare data is subject to a nearly infinite array of vulnerabilities. READ MORE: Turning Healthcare Big Data into Actionable Clinical Intelligence. Healthcare professionals can, therefore, benefit from an incredibly large amount of data. Additionally, some patient protection acts have failed to enlist fitness trackers, social media sites, and credit card payments under its data privacy clauses. Cloud storage is becoming an increasingly popular option as costs drop and reliability grows. North America and Europe have done especially well by enacting country-specific laws. Many organizations end up with a hybrid approach to their data storage programs, which may be the most flexible and workable approach for providers with varying data access and storage needs. Those categories were: Big data analytics in healthcare involves many challenges of different kinds concerning data integrity, security, analysis and presentation of data. Change within healthcare system is rather slow and takes time, but the solution inherently lies within the medical education system. Clinicians decisions are becoming more and more evidence-based meaning in no other field the big data analytics so promising as in healthcare. For healthcare organizations that successfully integrate data-driven insights into their clinical and operational processes, the rewards can be huge. Most healthcare organizations are not familiar with basic concepts of data warehouses. are posing credibility threats to data solutions for organizations. Healthcare providers are intimately familiar with the importance of cleanliness in the clinic and the operating room, but may not be quite as aware of how vital it is to cleanse their data, too. Understanding the volatility of big data, or how often and to what degree it changes, can be a challenge for organizations that do not consistently monitor their data assets. And what obstacles are encumbering its progress? In order to develop a big data exchange ecosystem that connects all members of the care continuum with trustworthy, timely, and meaningful information, providers will need to overcome every challenge on this list. Issues with data capture, cleaning, and storage While many organizations are most comfortable with on premise data storage, which promises control over security, access, and up-time, an on-site server network can be expensive to scale, difficult to maintain, and prone to producing data siloes across different departments. Healthier patients, lower care costs, more visibility into performance, and higher staff and consumer satisfaction rates are among the many benefits of turning data assets into data insights. Using Visual Analytics, Big Data Dashboards for Healthcare Insights. It will be long way before healthcare providers understand the value of big data. For future research, these challenges will be focused on and a novel framework will be built to include all the necessary steps for accurate medical big data … READ MORE: Which Healthcare Data is Important for Population Health Management? One clear illustration of the challenge is in one of the most promising areas of data analytics: clinical decision support. Healthcare data management is a gargantuan task, considering all the millions of patients, healthcare workers, and facilities involved. And even if data is held in a common warehouse, standardization and quality can be lacking. Key Big Data Challenges for The Healthcare Sector. Predictive Analytics Offers Insight into COVID-19 Spread, Disparities, Patient-Centered Medical Home Growing Among Medicaid Practices. Please fill out the form below to become a member and gain access to our resources. According to the Society of Actuaries (SOA), healthcare payers use the predictive big data analytics to pinpoint high-cost patients. At the point of care, a clean and engaging data visualization can make it much easier for a clinician to absorb information and use it appropriately. Results: A total of 58 articles were selected as … All data comes from somewhere, but unfortunately for many healthcare providers, it doesn’t always come from somewhere with impeccable data governance habits. Metadata allows analysts to exactly replicate previous queries, which is vital for scientific studies and accurate benchmarking, and prevents the creation of “data dumpsters,” or isolated datasets that are limited in their usefulness. Poor EHR usability, convoluted workflows, and an incomplete understanding of why big data is important to capture well can all contribute to quality issues that will plague data throughout its lifecycle. Predictive analytics is the branch of analytics that recognize patterns and predict future trends from information extracted from existing data … Doing so will take time, commitment, funding, and communication – but success will ease the burdens of all those concerns. Healthcare system has evolved once with technology, trying to improve the quality of living and save human lives. Healthcare organizations should assign a data steward to handle the development and curation of meaningful metadata. He obtained his PhD from the Faculty of Human and Social Development – Health Information Science at the University of Victoria in Canada. The great role comes with many critical concerns and responsibilities. Which Healthcare Data is Important for Population Health Management? As the volume of healthcare data grows exponentially, some providers are no longer able to manage the costs and impacts of on premise data centers. Firstly, traditional computing power cannot process these large amounts of data. Is Regulation Really Imperative: Cases For and Against. Image Credit: everything possible / Shutterstock. Data cleaning – also known as cleansing or scrubbing – ensures that datasets are accurate, correct, consistent, relevant, and not corrupted in any way. As a data-rich sector, healthcare can potentially gain a lot from implementing analytics solutions. Data extraction: Two reviewers independently extracted information on definitions of Big Data analytics; sources and applications of Big Data analytics in healthcare; challenges and strategies to overcome the challenges in healthcare. It also builds predictive models using data mining techniques for the future healthcare research. Common examples of data visualizations include heat maps, bar charts, pie charts, scatterplots, and histograms, all of which have their own specific uses to illustrate concepts and information. Though regulation exists, you may be finding that different hospitals are adopting different procedures when it comes to the privacy of health information. This is where we envision the medical profession to move from a disease-focused approach to a human-centered approach. Personalization of health means soliciting data from DNA, socio-demographic statistics, wearables, and even environmental factors. Fundamental differences in the way electronic health records are designed and implemented can severely curtail the ability to move data between disparate organizations, often leaving clinicians without information they need to make key decisions, follow up with patients, and develop strategies to improve overall outcomes. Understanding when the data was created, by whom, and for what purpose – as well as who has previously used the data, why, how, and when – is important for researchers and data analysts. Register for free to get access to all our articles, webcasts, white papers and exclusive interviews. Here are of the topmost challenges faced by healthcare providers using big data. But, there is an equal amount of obstacles in implementing predictive analytics in healthcare which need to be addressed: What are some of the top challenges organizations typically face when booting up a big data analytics program, and how can they overcome these issues to achieve their data-driven clinical and financial goals? Health systems can shorten the time-value curve of analytics with an applied healthcare analytics team. Hence, creating an end-to-end encrypted environment for data is necessary. What Is Deep Learning and How Will It Change Healthcare. The ability to query data is foundational for reporting and analytics, but healthcare organizations must typically overcome a number of challenges before they can engage in meaningful analysis of their big data assets. Even if providers could streamline the challenges of sending sensitive information across state lines, they still cannot be sure that the data will be attributed to the right patient on the other end. While data analytics could greatly improve the clinical decision-making process, the development of decision support tools hasn’t paid sufficient attention to how decisions are actually made and the related workflows supporting those decisions. They were also asked to then work in a group and develop 3 solutions to overcome the top challenges they identified. In the absence of medical coding systems like ICD-10, SMOMED-CT, or LOINC that reduce free-form concepts into a shared ontology, it may be difficult to ensure that a query is identifying and returning the correct information to the user. What Are Precision Medicine and Personalized Medicine? Objective: The purpose of this review was to summarize the challenges faced by big data analytics and the opportunities that big data opens in health care. The ultimate trophy? Healthcare is one such industry where most of the healthcare centers are focusing on data warehousing and clinical data repositories for predictive analysis. In practice, these safeguards translate into common-sense security procedures such as using up-to-date anti-virus software, setting up firewalls, encrypting sensitive data, and using multi-factor authentication. This means that sharing data with external partners is essential, especially as the industry moves towards population health management and value-based care. A great deal of the reporting in the healthcare industry is external, since regulatory and quality assessment programs frequently demand large volumes of data to feed quality measures and reimbursement models. Although big data analytics in healthcare has great potential, the discussed challenges need to be addressed and solved to make it successful. But even the most tightly secured data center can be taken down by the fallibility of human staff members, who tend to prioritize convenience over lengthy software updates and complicated constraints on their access to data or software. So far, we have seen many different examples of how healthcare institutions and providers are using novel technologies to make better decisions, accelerate their operations, and ultimately deliver a better experience to patients. They look at various patient details such as age, gender and spending history. That is what the field of Big Data is now trying to achieve — to look at new ways of combining traditional and non-traditional sources and use algorithms to find data patterns to improve patient monitoring, disease surveillance, treatment prescriptions, and patient care. Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is an ongoing battle for organizations, many of which aren’t on the winning side of the conflict.In one recent study at an ophthalmology clinic, EHR data ma… Healthcare organizations must frequently remind their staff members of the critical nature of data security protocols and consistently review who has access to high-value data assets to prevent malicious parties from causing damage. Healthcare data is not static, and most elements will require relatively frequent updates in order to remain current and relevant. Complete your profile below to access this resource. Healthcare organization recipients of HIMSS Davies Awards “consistently and constantly discuss the challenge of turning raw data into meaningful information,” she says. Data may also be reused or reexamined for other purposes, such as quality measurement or performance benchmarking. While all of this is changing the healthcare industry for the better, it is not that easy to reap the benefits of big data. With the large-scale pervasiveness of unconventional data collection practices comes the need for some form of oversight. HealthITAnalytics.com is published by Xtelligent Healthcare Media, LLC, Understanding the Many V’s of Healthcare Big Data Analytics, Turning Healthcare Big Data into Actionable Clinical Intelligence, clinical documentation improvement programs. These factors and more help to determine whether a patient should be …