Based on 25 years of healthcare IT experience, Dale outlines a detailed set of criteria for evaluating clinical analytic vendors. These criteria include 1) completeness of vision, 2) culture and values of senior leadership, 3) ability to execute, 4) technology adaptability and supportability, 5) total cost of ownership, 6) company viability, and 7) nine elements of technical specificity including data modeling, master data management, metadata, white space data, visualization, security, ETL, performance and utilization metrics, hardware and software infrastructure.
Chilmark’s 2017 Healthcare Analytics Market Trends Report is a trove of insights to the analytics solutions driving the management of population health and the transition to new reimbursement models.
The report reviews the analytics market forces at work, such as:
The need to optimize revenue under diverse payment models.
The increasing importance of analytics in general, and a platform in specific, that can aggregate all data.
Continuing confusion about how to react to MIPS and APMs.
The growing importance of providing a comprehensive set of open and standard APIs.
The need for better tools to create analytics-ready data stores.
The report is also a succinct guide to the 17 leading analytics vendors (which represent EHR, HIE, payer, and independent categories) with the most promising products, technology, and services offerings in the market.
Healthcare organizations rely on data to support informed decisions. To be truly valuable, data must be high quality and meet two criteria for end-users:
Data must be transformed from its raw, obscure form into actionable insights.
Data-driven insights must be immediately accessible at the point of care (versus in static dashboards or buried on the intranet).
Closed-Loop Analytics™ methodology transforms raw data into actionable, accessible insight—providing physicians and nurses with critical insight into their patients’ situation and how they can effectively intervene. A Closed-Loop Analytics approach will become increasingly essential as healthcare becomes more systems dependent.
Given the fact that up to 80 percent of clinical data is stored in unstructured text, healthcare organizations need to harness the power of text analytics. But, surprisingly, less than five percent of health systems use it due to resource limitations and the complexity of text analytics.
But given the industry’s necessity to use text analytics to create precise patient registries, enhance their understanding of high-risk patient populations, and improve outcomes, this executive report explains why systems must start using it—and explains how to get started.
Health systems can start using text analytics to improve outcomes by focusing on four key components:
Optimize text search (display, medical terminologies, and context).
Enhance context and extract values with an NLP pipeline.
Always validate the algorithm.
Focus on interoperability and integration using a Late-Binding approach.
This broad approach with position health systems for clinical and financial success.
When it comes to maximizing analytics ROI in a healthcare organization, the more domains, the merrier. Texas Children’s Hospital started their outcomes improvement journey by using an EDW and analytics to improve a single process of care. It quickly realized the potential for more savings and improvement by applying analytics to additional domains, including:
Organization-wide clinical improvement
The competencies required to launch and sustain such an organizational sea change are all part of a single, defining characteristic: the data-driven culture. This allows fulfillment of the analytics strategy, ensures data quality and governance, encourages data and analytics literacy, standardizes data definitions, and opens access to data from multiple sources.
This article highlights the specifics of how Texas Children’s has evolved into an outcomes improvement leader, with stories about its successes in multiple domains.
Healthcare data is positioned for momentous growth as it approaches the parameters of big data. While more data can translate into more informed medical decisions, our ability to leverage this mounting knowledge is only as strong as our data strategy. Hadoop offers the capacity and versatility to meet growing data demands and turn information into actionable insight.
Specific use cases where Hadoop adds value data strategy include:
Risk stratification is essential to effective population health management. To know which patients require what level of care, a platform for separating patients into high-risk, low-risk, and rising-risk is necessary. Several methods for stratifying a population by risk include: Hierarchical Condition Categories (HCCs), Adjusted Clinical Groups (ACG), Elder Risk Assessment (ERA), Chronic Comorbidity Count (CCC), Minnesota Tiering, and Charlson Comorbidity Measure. At Health Catalyst, we use an analytics application called the Risk Model Analyzer to stratify patients into risk categories. This becomes a powerful tool for filtering populations to find higher-risk patients.
As vice president of technology for a healthcare IT company, I’m often asked what should be considered when selecting a solution for healthcare analytics. Healthcare organizations have many choices when selecting a healthcare data warehouse and analytics platforms. I advise them to consider the following fundamental criteria: 1) time-to-value (measured in months, not years), 2) experience as a predictor of future success, and 3) extensibility to meet your needs.
We hand-picked the most interesting, useful, credible factoids from 2015 (including the plethora of facts that came out of the 2nd Healthcare Analytics Summit) to create an easy-to-share presentation. The 32 factoids included in this presentation revealed several interesting healthcare trends:
Trend #1: Healthcare analytics continue to improve outcomes and save money. For example, OSF’s predictive readmission model reduced its all-cause readmission rate to less than 10%.
Trend #2: New technologies are improving patient engagement. For example, 73 percent of health executives surveyed see positive ROI from personalization technologies, and 76 percent of doctors say patient use of wearable health devices improves engagement.
Trend #3: Patients and providers agree on data is useful but have security and interoperability concerns. For example, 83 percent of patients don’t trust EHR safety and security, and 83 percent of physicians are frustrated by EHR interoperability.
Although a majority of healthcare leaders understand the importance of using analytics to improve outcomes and reduce costs, only 15% of hospitals use predictive analytics. We hope to see analytics use increase in 2016, and we’re excited to see how technology will continue to engage patients and lead to better health outcomes.
Analysts are most effective when they have the right tools. In healthcare, that means providing data analysts with a means of accessing and testing ALL of the available data and using it to discover more insights. To do this, analysts need guidance more than they need a detailed set of instructions. And, equally as important, they need a data warehouse and access to a testing environment and data discovery tools, so they can truly do the work they were hired to do: analyze.
Improving Patient Safety and Quality through Culture, Clinical Analytics, Evidence-Based Practices, and Adoption
According to the Centers of Disease Control (CDC), an estimated 70,000 patients die each year from hospital-associated infections (HAIs): contrast the CDC statistic with the fact that only 35,000 people die each year in the U.S. from motor vehicle accidents. Learn key best practices in patient safety and quality including: patient safety as a team sport, the added challenges of healthcare being the most complex, adaptive system, and how culture, analytics, and content contribute to improve outcomes and lower costs.
Value-based care has remade the healthcare landscape for small hospitals. Many are struggling to compete with the larger, better-funded medical centers in the communities they serve. Embracing data and analytics is no longer a luxury for these organizations if they are to succeed and remain competitive. Data analysis can assist senior leaders in identifying opportunities for improvement while balancing long-term goals with short-term pressures. Incorporating data in to the culture and making it a part of everyday decision making will enable smaller hospitals to not only survive, but thrive in the new era of value-based care.
Retrospective and predictive analytics are familiar terms for practitioners of clinical outcomes improvement, but the new kid on the block is prospective analytics. This is the next level that uses findings from its predecessors to not only identify the best clinical routes, but also what the results might be of each choice. Prospective analytics gives bedside clinicians an expanded, branching view of operational and clinical options in a type of decision support that can lead to not only improving surgical and medical outcomes, but to making a positive financial contribution, as well. But, as expected with any new process or new way of thinking, prospective analytics requires careful introduction and stewardship to help drive its adoption within the organization.
Deloitte Report Reveals Why Health Systems Lack Integrated Healthcare Analytics Strategy; Recommends Adoption Framework
A 2015 Deloitte Report investigates why health systems lack integrated healthcare analytics strategies (despite acknowledging the myriad of benefits analytics-driven insights offer):
Lack clarity on current analytics spending.
Culture, operating models, and fragmented oversight.
Lack of access to funding and skilled resources.
Numerous confusing vendor product offerings.
Inconsistent industry definitions of analytics.
The report concludes by recommending analytics adoption guidelines, from engaging committed leaders across the enterprise and implementing a structured data governance model to emphasizing data and technology standards to promote interoperability.
The availability of healthcare IT solutions can be overwhelming and all promise to solve an organization’s most pressing issues. While typical data and analytic applications are excellent at exposing opportunities for improvement that are impacting the bottom line, most are not effective at helping the organization determine what to do to address them and improve outcomes. However, a new approach to creating analytics applications is emerging. Analytics applications that incorporate best practices clinical content along with the best practices visualizations help everyone understand the problem and the solution. These applications also enable clinicians to better understand, adopt, roll out, and execute outcome improvement initiatives with healthcare systems. Health Catalyst has deliberately created a comprehensive, dynamic suite of applications that integrate clinical content and facilitate the orderly implementation of action plans.
When an analyst from another health system asked our resident analytics expert about the practical value of the Analytics Adoption Model, our expert had a lot to say. Specifically, he elaborated on the results the organization would realize, especially if they used the Adoption Model as a roadmap on their journey to become data driven. But first, they would need to adopt a late-binding data warehouse and analytics applications. With both solutions, they would be able to confidently deliver evidence-based care.
Better analytics technology alone will not achieve the healthcare industry’s desired improvements in life, efficiency, effectiveness, or provider and patient satisfaction. That all will change, however, when the technology is combined with a deployment system. With such a system, health systems learn from experienced healthcare experts about how to use the information from their analytics applications to transform from the old world to the new.
Healthcare analytics are essential for organizations to thrive in the new healthcare environment. Using analytics, systems can evaluate efficiency, effectiveness, and find improvement opportunities. There are two principal approaches: outsourcing the analytics function to benchmarking companies and providers of software-as-a-service; and doing analytics in-house with a system’s own data warehouse. The pros of outsourcing include gaining benchmarking access to how health system peers are performing. The cons to outsourcing include focusing too much high-level outcomes with no insight in how to effect change. The pros of in-house analytics include having quick access to fine-grained details of the data and being able to include clinicians in the implementation and development of the analytics process. A con is that in-house analytics can require significant resources – an investment in the right personnel and right technology.
As healthcare leaders look into ways to bend the cost curve in today’s value-based reimbursement environment, they are faced with many vendors offering analytics solutions. The key is to determine which product best fits the organization’s needs, not just now but for the long term. This article discusses the five primary options available and the pros and cons of each one. The five analytics options include the following: (1) Buy from a hosted analytics service provider. (2) Buy from a large non-healthcare-specific technology vendor. (3) Buy “best of breed” point solutions. (4) Buy from the EMR vendor. (5) Buy and build an analytics solution from a healthcare data warehouse platform vendor.
Healthcare organizations are recognizing the value of healthcare analytics, especially in their Big Data, population health management, or accountable care initiatives. This is good because without analytics it is difficult to impossible to run these programs successfully. That said, analytics are not the magic bullet and proper process must be in place. The three most common mistakes health systems makes with their healthcare analytics are: 1. Analytics Whiplash- when the analytics goes from one project to another without being able to fully understand the data and what it’s saying. 2. Coloring the Truth- When analysts don’t feel like they can be completely forthcoming with information and only give leadership the news they want to hear. 3. Deceitful Visualizations- Manipulating charts, graphs, and the like to reflect what the analyst or leadership wants the data to say, rather than what it actually says.
Analytics are supposed to provide data-driven solutions, not additional healthcare analytics pitfalls and other related inefficiencies. Yet such issues are quite common. Becoming familiar with potential problems will help health systems avoid them in the future. The three common analytics pitfalls are point solutions, EHRs, and independent data marts located in many different databases. An EDW will counter all three of these problems. The two inefficiencies include report factories and flavor of the month projects. The solution that best overcomes these inefficiencies is a robust deployment system.
Providers throughout the U.S. are facing difficult choices for their healthcare analytics applications: should they use an out-of-the-box solution or put in the extra time, effort, and expense to develop a customized solution? Out-of-the-box healthcare analytics applications are just that — they’re applications most health systems can use as-is because the application is designed to work well with the popular source systems in the marketplace. To really gain a deep understanding of the organization and its patients, though, customization of the analytics application is necessary. Customization enables analysts to dig much deeper into the data — and not just after the initial implementation. Instead, the best type of customized healthcare analytics applications solutions can accommodate endless customizations time after time based on new definitions and rules. By selecting customized applications, health systems will get made-to-order analytics that will provide a return on investment — now and in the future.
There’s never been a more critical time in the history of U.S. healthcare for providers to turn to clinical analytics to help them survive and thrive amidst healthcare reform. Clinical analytics can enable health systems with the following improvements as they shift from fee-for-service reimbursements to value-based purchasing: pinpoint waste reduction opportunities, identify specific margin improvement opportunities, systematically identify performance improvement opportunities, automate the tracking and reporting of quality measures, and understand the cost structure at a granular level. Clinical analytics can also help ACOs unlock the data from their EMR investment and provide real-time data for providers and payers to work together to provide value-based insurance design.
Healthcare informatics has come a long way since its founding visionaries saw a way to use technology to extract healthcare data to improve patient care. But a new era has arrived and health systems are now facing the new challenge of maintaining massive amounts of powerful data that’s sitting unused in expensive storage. The next phase of healthcare informatics is for health systems to move from data acquisition to data extraction, so they can use the insights of the data to prioritize which areas will benefit the most by using data to improve quality and reduce costs.
This is the complete 4-part series demonstrating real-world examples of the power of data mining in healthcare. Effective data mining requires a three-system approach: the analytics system (including an EDW), the best practice system (and systematically applying evidence-based best practices to care delivery), and the adoption system (driving change management throughout the organization and implementing a dedicated team structure). Here, we also show organizations with successful data-mining-application in critical areas such as: tracking fee-for-service and value-based payer contracts, population health management initiatives involving primary care reporting, and reducing hospital readmissions. Having the data and tools to use data mining and predict trends is giving these health systems a big advantage.