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.
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.
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.
After working with many healthcare organizations to help them implement the appropriate EDW for their needs, we’ve learned how important it is to create cross-functional teams from across the organization. Why? These cross-functional teams will simultaneously improve clinical and financial outcomes and demonstrate ROI. By following this approach, you’ll experience the following advantages:
Removal of organizational barriers between team members
Prioritization of BI and analytic efforts according to institutional readiness and need
Engagement and prioritization from appropriate leadership
Buy in from each level of the organization to improvement goals
We are excited to announce the Healthcare Analytics Summit, on September 24-25, in Salt Lake City. This summit will be different from any conference or analytics forum you may have attended. You will hear from the best thinkers from across the industry. You will experience analytics-rich breakout sessions. Our goal is to make this the best, most educational and practical healthcare analytics conference on the planet. Unlike any other summit, this experience will be driven by data using live session polling, real-time analysis, and advanced event applications at your fingertips so you can touch, taste, and breathe analytics. We hope that HAS ‘14 will accelerate your application of analytics best practices in your current role and avoid the time-consuming and costly pitfalls of others who learned by trial and error.
Recently Gartner came out with a seminal healthcare IT report with a striking recommendation. They acknowledged the all-consuming effort that has surrounded the wave of EMR adoption and rollouts. However, they suggested a new wave was coming. They recommended that once an EMR was rolled out, the top IT priority was to develop an enterprise-class data warehouse. They also listed 5 classic mistakes that could be avoided based on 25 years of data warehousing experience. Read further to get the highlights and a free copy of the actual report.
Most organizations purchase a point solution because they’re feeling a particular pain, and they want it to stop. They may have other pains as well, but they don’t notice them at the time. Once they fix the first pain another may crop up, so they purchase a point solution for that. And so it continues until they have all these individual solutions. It’s like a physician treating individual symptoms instead of looking at the entire body to see if there is something bigger going on.
Health data analytics is the third wave of health IT we’re undertaking, right after data capture and data sharing. Having excellent analytics capability will provide the return on investment for the massive amounts of spending happening in health IT in the past few years. Buzzwords, like “big data” and “analytics” are becoming commonplace., however this also takes away from their effectiveness. According to the Gartner Hype Cycle technology has five key phases in its lifecycle: Technology Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slop of Enlightenment, and Plateau of Productivity. Now that we have widespread adoption of EHRs, the superior use of analytics will be a dominant factor for success over the next four to five years.
HIMSS 2014 day 3 showed a variety of analytics in different settings: surgical teams, clinical integration, ICD-10, children’s hospitals and academic medical centers and population health.
The second day of HIMSS 2014 was jam packed with many activities and more information related to healthcare analytics. data warehousing, data governance, ACO learnings, and predictive analytics …
In this article, Brian Ahier – a Health Catalyst guest contributor and industry expert – predicts that 2014 will prove to be the year of healthcare data analytics. There will be a marked shift away from volume and toward value for both healthcare delivery and payments. ACOs and Patient-centered Medical Homes will flourish. 2014 will be a perfect storm of regulatory change, business drivers, and technology solutions. The ACA established the value-based purchasing program and it will be essential to leverage healthcare data effectively to drive value-based decision-making. Predictive analytics solutions can generate and evaluate hypotheses, and determine a confidence level for the hypotheses. But comparative analytics, predictive analytics, and NLP will not solve all of health care’s problems. A successful organization must have tools with the ability to score predicted outcomes to better guide the care team on the need to intervene, when and how to intervene, and a feedback loop to create a learning healthcare system.
Looking ahead, 2014 feels like the turning point for analytics: those who have invested smartly will find themselves at a competitive advantage; those who haven’t will find themselves playing catchup.
Prediction #1: Health systems that invested in data warehousing as the foundation of their analytics strategy will emerge as industry and market-share leaders.
Prediction #2: Health systems that have not yet made a data warehouse investment will look for quick answers in their EHRs.
Prediction #3: Health systems will soon realize that EHR providers can’t provide the help they need for healthcare analytics.
Prediction #4: When it comes to analytics, no organization will be able to afford to sit on the fence.
Small healthcare technology companies are better at providing healthcare-specific solutions than large, non-industry-specific technology companies. The reasons for this are multifaceted.
i. Patience- Healthcare is a cautious industry and healthcare providers like to start small.
ii. Fragmentation and Skepticism- There are many market segments in the healthcare industry, and newly evolving segments are crowded and confusing.
iii. Brand defocus- Larger companies need to consider the entire company, including the non-healthcare-focused business units
iv. Best-of-Breed vs. Corporate Branding- Business units within large, multi-brand technology companies are compelled to sell their corporate products rather than the true “right tool for the job.”
v. Deal Structure- Large companies have very structured sales and contracting processes that are not well-suited to the flexibility and adaptability needed for the ever-evolving healthcare industry.
Guest contributor, Brian Ahier, describes the transition from “meaningful use” to meaningful analytics and achieving high-quality care. Since meaningful use is requiring greater interoperability and data sharing, there is now much greater opportunity to aggregate data at a community level and have an even broader data set than just the EHR to mine for clinical intelligence. One benefit from HIE, besides improved care coordination, is the ability to perform queries and apply analytical tools to those data that were not previously available. The five health outcomes policy priorities included in meaningful use are:
1. Improve quality, safety, efficiency and reduce health disparities
2. Engage patients and families
3. Improve care coordination
4. Improve population and public health
5. Ensure adequate privacy and security protections for personal health information
As a business person and a CIO, the only two metrics that really matter to me are employee satisfaction and customer satisfaction. As fellow CIOs can attest, we are inundated with metrics. Managing a complex IT environment in a healthcare setting is like surfing in a hurricane of metrics, at every layer of technology that we manage, from the data center to the software application. But… the only two metrics that really matter are employee satisfaction and customer satisfaction. Every other metric is a means to those two ends.
Healthcare has been slowly moving through three waves of digitization and health data management: data collection, data sharing, and data analytics. Data collection and sharing waves have been having some success, spurred on by the HITECH Act and implementation of electronic health records and health information exchanges. They have not yet significantly impacted costs or quality in healthcare. The third wave of analytics is ready to crash on our shores and I believe we will actually begin to see an IT infrastructure that support the new payment and care delivery models which are emerging. Guest blogger Brian Ahier explains how healthcare can work to realize the value of their IT systems and the healthcare analytics adoption model.
All of us quietly yearn to be heroes. CIOs are no exception. We want to harness the power of healthcare analytics, using information technology to dramatically improve healthcare quality and costs. Despite their privileged position atop the IT food chain, though, only a handful of healthcare CIOs ever get to realize this dream. Why? Simply put, CIOs never own both the data content and application layers of any meaningful technology, at the business transformation level. Which is why the Enterprise Data Warehouse (EDW) represents a CIO’s last chance to be a transformational hero in healthcare.
Texas Children’s Hospital, a client of mine, found they needed an immediate solution to address their labor and productivity challenges….The Health Catalyst Labor Productivity Advanced Application delivered a view of staffing levels, volume and productivity across Texas Children’s various cost centers. The application enables the business and unit managers to track and manage their resources. It delivers information into the hands of decision makers to help them manage their business. Managers can track performance as often as daily to see exactly how well labor is being allocated and make rapid modifications to counter scheduling problems. In the event that labor utilization outpaces volume, managers can drill further into labor data to understand utilization at the job code level.
Baffled by the options for healthcare data warehouses? Here, Eric compares two models: Late-Binding™ and EMR-based. Many organizations are taking a wait-and-see approach with analytics solutions provided by EMR vendors and other out-of-the-box solutions. In this post, Eric compares two models of a data warehouse: Late-Binding™ and EMR-based. He also outline important factors to consider when planning for long-term success in data warehousing and analytics.
3 Reasons Why Comparative Analytics, Predictive Analytics, and NLP Won’t Solve Healthcare’s Problems
I had a recent opportunity to engage in an online discussion with a well-known healthcare analytics vendor about the value of comparative analytics, predictive analytics, and natural language processing (NLP) in healthcare. This vendor was describing a beautiful new world of the future, in which comparative data, in particular, would be the cornerstone of our industry’s turnaround. The executive summary of my response: “Beware the smoke and mirrors” because 1) comparative data doesn’t drive improvement, 2) predictive analytics fails to include outcomes, 3) gaps in industry healthcare data limits the effectiveness of NLP.
One of the major contributing factors to escalating hospital costs is patient variation and waste associated with the delivery of care. Hospitals have begun to address waste through a variety of methods such as Six Sigma, LEAN and other healthcare quality process improvement techniques. While these methods are effective at dealing with administrative costs, a much greater return can be gained by concentrating on the clinical or patient care costs. Clinical work teams coupled with data and healthcare analytics reduce costs by helping your organization reduce variation, leading to lowering cost trends as the revenue trend flattens. To fully understand your costs and identify areas of waste, you need good data.