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As healthcare providers transition from fee-for-service to value-based care models and continue to look for ways to be more efficient in their day-to-day business, they are discovering new and innovative analytic models to identify high-risk patients, deploy evidence-based medicine and reduce adverse effects and infections. Therefore, they have to assemble information generated across the overall health and administrative systems, including data imported from outside sources and services. In order to assemble this information efficiently and correctly, Big Data is often presented as a potential solution. However, especially in the case of a healthcare system, this data analysis is quite complex. Nevertheless, healthcare in some cases are presenting a very impressive use of analytics. In addition to the traditional providers like Siemens, GE, Philips, EPIC and Intersystems, several high-tech providers like Apple, IBM, Microsoft, Oracle and Google have been increasing investment in this area by buying startups, which are accelerating the healthcare digital transformation. What Kind of Data is Available in a Healthcare System? Healthcare tends to lag behind other industries in terms of IT governance and maturity. Effective use of analytics is not something one can buy from a vendor like medical ultrasonography equipment. It's an organizational and cultural value that has to grow and mature within the health system. In addition, its complexity is magnified by the shift the analytics have to make from merely reporting data to predicting data. Big Data is commonly defined based on 3 V’s: large Volumes of data, Variety of data formats and high Velocity of processing. The common notion is that bigger is better when it comes to data and analytics, which implies high complexity and high investment required. This is not always the case. MIT professor Michael Stonebraker presented in his research that 69 percent of corporate executives named greater data variety as the most important factor. Therefore, many firms see the largest opportunity in Big Data from the use of traditional data sources that have gone untapped in the past. These are data sets that have typically sat outside the purview of traditional data warehouses – the “long tail” data. Most hospitals have already implemented an Enterprise Resource Planning system, a Computerized Provider Order Entry, a Laboratory Information System, a Picture Archiving and Communication System, a Radiology Information System, an Electronic Health Record, a Medical Mobile Dictation system, a Health Information Exchange and/or an Electronic Medical Health suite, among others. Initially, it seems that the data available from these systems’ databases would be very valuable. Additionally, there are external data (population, demography, georeference data, illness occurrences, payers’ data, etc.) that can be aggregated to improve the analytic process. However, in most of the cases, each system was implemented without data integration in mind and in different sequencing with old or new standards. Not all information is recorded by the medical staff, just the minimum required by the systems. Several areas of data are in fact in free text format. Some tables like medicine prescription are not normalized or even loaded at the systems. Therefore, the actual business readiness (staffing and maturity level), the business capabilities (stable processes in place) and the technical capabilities (available IT software and hardware infrastructure) should be assessed. Assessing a Healthcare Organization’s Readiness It is necessary to recognize that the hospital or clinic should be managed based on data and, moreover, its medical efforts can be improved significantly by meaningful use of available data. First, a list of priorities and problems should be developed. Design thinking or other methodology could be applied to define potential solutions for each problem. At this point, the organization will be able to evaluate what is needed in order to achieve its goals. A business plan should be developed and approved by the board of the organization, as this program will likely affect several departments within the hospital. The discussions derived from this approval process will be important to develop the strategic and managerial objectives of the program, share knowledge about the subject and engage the top and middle management in addition to key stakeholders like doctors, nurses and the administrative staff. A BI system may be enough to use as a starting point. In one case, a large healthcare provider adopted a BI solution, which allowed users to identify previously unknown evidence and indicators, helping them to meet the requirements of each problem area they had identified. In addition, this contributed to maximizing the company's revenue. Among several indicators, it was able to be accessed quickly – both the production as the cost of healthcare provider network, as well as identification of behavioral differences (the outliers) with providers. It was a huge advance for them. The hospital must consider developing some core internal skills before pursuing such a program, such as establishing a proper and small competence center. An IT program always involves a learning curve for all. At times, full IT outsourcing contracts fail due to the client lack of internal skills to run this kind of operation. In general, the data should start small, but the data needs to be concise and address the strategic healthcare problem being measured, not just the data that's easiest to obtain. While data that comes easy may make the project go faster, the analytic results are likely to have only limited value, which can jeopardize the whole program. Another approach should be to consider cloud computing instead of building upon the healthcare system’s IT infrastructure from the beginning, based on the benefits of the cost per demand, processing capacity and HIPAA compliance. Several companies are providing cloud-based analytics platforms with several APIs for easy usage and maintenance. Although it is not without cost, it is much easier to implement than modifying some existing infrastructure, and requires much less expense initially. An Evolving Road Map Once the strategic objectives are broken down into managerial objectives and goals, they can be part of each stage of the overall road map. The implementation program should build an iterative process of understanding data and applying it to business goals, looking for the following dimensions: financial, operational, clinical and behavioral. For each step, one should identify available data and assess its quality level, gather the business needs and technical options, define the technical solution, and test the potential results, using feedback from the experience for the next iteration. The idea is to establish a data science practice. Based on the resulting road map, a complete IT architecture can be drawn as a reference model. However, it’s recommended to dissect the technology part of the map into simpler components that can be built and tested, considering the learning curve of the business and IT staff, and also the partners, staff and/or technologies. In addition, some architecturally significant decisions should be made, such as: cloud versus on-premise (privacy of data, latency, volume, cost), which technical platform to use, how to connect your existing clinical and enterprise data warehouses to it and how to integrate with existing services. Challenges in Implementing Conceptually, an Enterprise Master Patient Index (EMPI) is a database that is used across a healthcare organization to maintain consistent, accurate and current demographic and essential medical data on the patients seen and managed within its various departments. The patient is assigned a unique identifier that is used to refer to this patient across the enterprise. Therefore, the patient record must be identified individually across all data sets. However, this is not true everywhere, and keeping the data quality high on all data sets is not a trivial matter. Clinical data contains codes using different coding schemes like CPT, ICD9, ICD10 or NDC and also may contain non-standard coding schemes, requiring translation services in order to have a common dictionary for the data analysis. Data sources from the healthcare systems provide flat files in HL7 format that are not immediately useful for data processing. This then requires de-serializing and exposing HL7 messages as pre-defined structures to the rest of the clinical processing pipeline. Data about a given population is normally split across multiple flat files that contain hundreds of thousands of rows. However, not all data is accessible for processing due to the complexity it introduces to existing Extract-Transform-Load processes adopted by the BI systems. Traditionally, data visualization or reporting is developed based on a waterfall approach where an expert provides the requirements in the earlier phases of the process and verifies the final result at the end. In Big Data, the agile approach is much more appropriate, where multiple experts and the IT team must build up together the solution, in timely defined iterative job tasks, requiring more engagement and availability to the project. While technology is a critical aspect, developing strong data science capabilities (deep knowledge in statistics and of the specific domain of the problem to be solved) within the healthcare organization is equally important. Today, a competent data scientist is in high demand and is not always easy to find. In conclusion, the extensive usage of KPIs and dashboards has been promoting the adoption of BI platforms across industries and in healthcare recently. The complexities of achieving improved patient outcomes, regulatory compliance, and strategic business goals require healthcare organizations to become more proactive in the care of their patients. Current technologies have been allowing healthcare providers to execute data analytics across disparate systems running databases, data warehouses and structured or unstructured data sets, without impacting day-to-day operations or access to data. Some organizations in a more advanced stage are already applying machine learning in sophisticated diagnosis or predictive analytics. Ultimately, healthcare providers can use information technology (Big Data or not) to optimize their analytics’ efficiency as they transition to value-based care. Moving forward, with data science based on predictive analytics, providers can deliver better patient outcomes, encounter fewer complications, decrease unnecessary emergency room interventions and experience higher levels of wellness across the population — and all at a lower cost. Article written by Werther Krause Image credit by Getty Images, Tim Pannell/Corbis/VCG Want more? For Job Seekers | For Employers | For Influencers
Have you ever noticed that organizations are increasingly using the terms “big data,” “analytics” and (to a somewhat lesser but still notable degree) “business intelligence” (BI) interchangeably? But they aren’t quite the same thing. Here’s how respected, authoritative sources define the three: Big data is a… “high-volume, high-velocity and/or high-variety information (asset) that (demands) cost-effective, innovative forms of information processing that enable enhanced insight, decision making and process automation,” according to Gartner . I like this one too, from the McKinsey Global Institute : “Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze. This definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered big data.” Analytics is… “the scientific process of transforming data into insight for making better decisions,” according to the Institute for Operations Research and the Management Sciences (INFORMS), the world’s largest organization for professionals in the field of operations research, management science and analytics. Business intelligence… “simplifies information discovery and analysis, making it possible for decision-makers at all levels of an organization to more easily access, understand, analyze, collaborate and act on information, anytime and anywhere,” according to Microsoft . I’m particularly intrigued with the last definition. It dates back to 2008, but it’s relevant today – especially when applied to what we call “people analytics,” a.k.a., the combining of big data, analytics and business intelligence to improve recruitment, onboarding, training/development, engagement, retention, succession and other human resources (HR) practices. Ultimately, you use people analytics to drive toward a better-performing workforce. Here’s how: By eliminating bottlenecks in recruitment cycles, thus reducing time-to-hire while creating a more positive experience for job candidates By bringing on a higher quality of employee – one who will stay with your organization for an extended period, therefore boosting retention By increasing job satisfaction, which directly leads to greater productivity and performance, along with a stronger sense of ownership among staffers in an employer’s overall mission and strategic goals. The best people analytics tools allow HR leaders and their teams to maximize the possibilities of existing data. It doesn’t necessarily have to be big data. But it often is, and this reality should only grow more prevalent as we collect more and more data, from more and more resources. By applying BI principles which foster the simplification of information discovery and analysis, effective solutions empower organizations to easily and instantly derive the most value from talent-management data. The tools are so intuitive and user-friendly that you don’t have to be a data scientist to make sense of them. As Microsoft described, decision-makers and staffers at all levels readily access, understand, collaborate and act upon the information. This proves critical because tech proficiency among HR professionals runs the range. Even if certain team members are IT savvy, it’s not a prerequisite. If the tools intimidate them, they won’t use them – and that’s a waste of your organization’s money and time. Strip away the intimidation, however, and these professionals find that they can vastly augment workflow processes, perhaps through one insight. People analytics might reveal, for example, a previously unknown redundancy or bottleneck that can be removed, saving literally thousands of dollars by expediting time-to-hire or improving onboarding practices. To extend the concepts of BI here, we must also avoid focusing solely upon the technologies. Always remember that this is about solving business problems. And, should HR teams truly take full advantage of the technologies, they can explore endless ways to apply people analytics to an endless number of business problems, such as low morale, inadequate training, high attrition, pending retirements, succession gaps, etc. Despite all the talk, people analytics is still in its infancy. There is a world of opportunity ahead. We will see, as organizations move forward, they will hire and retain better employees, leading to tangible and immediate ROI. What’s better: Your HR teams won’t need a degree in data science to accomplish all of this. Article written by Joe Abusamra Want more? For Job Seekers | For Employers | For Influencers
Interest in blockchain continues to be high, but there is still a significant gap between the hype and market reality. Only 11% of CIOs indicated they have deployed or are in short-term planning with blockchain, according to the Gartner, Inc. 2019 CIO Agenda Survey of more than 3,000 CIOs. This may be because the majority of projects fail to get beyond the initial experimentation phase. “Blockchain is currently sliding down toward the Trough of Disillusionment in Gartner’s latest ‘Hype Cycle for Emerging Technologies,’” said Adrian Leow, senior research director at Gartner. “The blockchain platforms and technologies market is still nascent and there is no industry consensus on key components such as product concept, feature set, and core application requirements. We do not expect that there will be a single dominant platform within the next five years.” To successfully conduct a blockchain project, it is necessary to understand the root causes for failure. Here are the seven most common mistakes in blockchain projects and how to avoid them: 1. Misunderstanding or Misusing Blockchain Technology Gartner has found that the majority of blockchain projects are solely used for recording data on blockchain platforms via decentralized ledger technology (DLT), ignoring key features such as decentralized consensus, tokenization, or smart contracts. “DLT is a component of blockchain, not the whole blockchain. The fact that organizations are so infrequently using the complete set of blockchain features prompts the question of whether they even need blockchain,” Leow said. “It is fine to start with DLT, but the priority for CIOs should be to clarify the use cases for blockchain as a whole and move into projects that also utilize other blockchain components.” 2. Assuming the Technology Is Ready for Production Use The blockchain platform market is huge and largely composed of fragmented offerings that try to differentiate themselves in various ways. Some focus on confidentiality, some on tokenization, others on universal computing. Most are too immature for large-scale production work that comes with the accompanying and requisite systems, security, and network management services. However, this will change within the next few years. CIOs should monitor the evolving capabilities of blockchain platforms and align their blockchain project timeline accordingly. 3. Confusing a Protocol With a Business Solution Blockchain is a foundation-level technology that can be used in a variety of industries and scenarios, ranging from supply chain over management to medical information systems. It is not a complete application as it must also include features such as user interface, business logic, data persistence, and interoperability mechanisms. “When it comes to blockchain, there is the implicit assumption that the foundation-level technology is not far removed from a complete application solution. This is not the case. It helps to view blockchain as a protocol to perform a certain task within a full application. No one would assume a protocol can be the sole base for a whole e-commerce system or a social network,” Leow added. 4. Viewing Blockchain Purely as a Database or Storage Mechanism Blockchain technology was designed to provide an authoritative, immutable, trusted record of events arising out of a dynamic collection of untrusted parties. This design model comes at the price of database management capabilities. In its current form, blockchain technology does not implement the full “create, read update, delete” model that is found in conventional database management technology. Instead, only “create” and “read” are supported. ”CIOs should assess the data management requirement of their blockchain project. A conventional data management solution might be the better option in some cases,” Leow said. 5. Assuming That Interoperability Standards Exist While some vendors of blockchain technology platforms talk about interoperability with other blockchains, it is difficult to envision interoperability when most platforms and their underlying protocols are still being designed or developed. Organizations should view vendor discussions regarding interoperability as a marketing strategy. It is supposed to benefit the supplier’s competitive standing but will not necessarily deliver benefits to the end-user organization. “Never select a blockchain platform with the expectation that it will interoperate with next year’s technology from a different vendor,” said Leow. 6. Assuming Smart Contract Technology is a Solved Problem Smart contracts are perhaps the most powerful aspect of blockchain-enabling technologies. They add dynamic behavior to transactions. Conceptually, smart contracts can be understood as stored procedures that are associated with specific transaction records. But unlike a stored procedure in a centralized system, smart contracts are executed by all nodes in the peer-to-peer network, resulting in challenges in scalability and manageability that haven’t been fully addressed yet. Smart contract technology will still undergo significant changes. CIOs should not plan for full adoption yet but run small experiments first. This area of blockchain will continue to mature over the next two or three years. 7. Ignoring Governance Issues While governance issues in private or permissioned blockchains will usually be handled by the owner of the blockchain, the situation is different with public blockchains. “Governance in public blockchains such as Ethereum and Bitcoin is mostly aimed at technical issues. Human behaviors or motivation are rarely addressed. CIOs must be aware of the risk that blockchain governance issues might pose for the success of their project. Especially larger organizations should think about joining or forming consortia to help define governance models for the public blockchain,” Leow concluded. Upcoming IT Symposium/Xpo Additional analysis on blockchain will be presented during Gartner IT Symposium/Xpo 2019 , a gathering of CIOs and other senior IT executives to gain insight into how their organizations can use IT to overcome business challenges and improve operational efficiency. Upcoming dates and locations include: September 16-18: Cape Town, South Africa October 20-24: Orlando October 28-31: Gold Coast, Australia October 28-31: Sao Paulo, Brazil November 3-7: Barcelona November 11-14: Goa November 12-14: Tokyo Article published by icrunchdata Image credit by Getty Images, DigitalVision, Shana Novak Want more? For Job Seekers | For Employers | For Influencers
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