Academic medical centers (AMCs) are a triple threat on the healthcare court with their combined medical center, education, and research sections. With a unique set of resources, AMCs have the ability to take a comprehensive, holistic approach to patient care. However, one of the challenges they still face is utilizing healthcare analytics effectively within the patient care setting. With the Healthcare Analytics Adoption Model and other data expertise, AMCs can learn how to merge siloed data, while improving operations, and delivering the highest quality of care to each patient.
Learn more about Dale Sanders
Dale has been one of the most influential leaders in healthcare analytics and data warehousing since his earliest days in the industry, starting at Intermountain Healthcare from 1997-2005, where he was the chief architect for the enterprise data warehouse (EDW) and regional director of medical informatics at LDS Hospital. In 2001, he founded the Healthcare Data Warehousing Association. From 2005-2009, he was the CIO for Northwestern University’s physicians’ group and the chief architect of the Northwestern Medical EDW. From 2009-2012, he served as the CIO for the national health system of the Cayman Islands where he helped lead the implementation of new care delivery processes that are now associated with accountable care in the US. Prior to his healthcare experience, Dale had a diverse 14-year career that included duties as a CIO on Looking Glass airborne command posts in the US Air Force; IT support for the Reagan/Gorbachev summits; nuclear threat assessment for the National Security Agency and START Treaty; chief architect for the Intel Corp’s Integrated Logistics Data Warehouse; and co-founder of Information Technology International. As a systems engineer at TRW, Dale and his team developed the largest Oracle data warehouse in the world at that time (1995), using an innovative design principle now known as a late binding architecture. He holds a BS degree in chemistry and minor in biology from Ft. Lewis College, Durango Colorado, and is a graduate of the US Air Force Information Systems Engineering program.
Read articles by Dale Sanders
Why would a healthcare data warehousing and analytics company partner with the life sciences industry? Because trust and collaboration across the industry—between life sciences, healthcare delivery systems, and insurance—is the only path to real healthcare transformation.
Health Catalyst recognizes an industrywide improvement opportunity in collaborating with life sciences to build mutual trust, integrate data, and leverage analytics insights for a common interest (i.e., patient outcomes). By aligning themselves around human health fulfillment, Health Catalyst, their provider partners, and life sciences will advance important healthcare goals:
Improving clinical trial design and execution.
Stimulating clinical innovation.
Supporting population health.
Reducing pharmaceutical costs.
Improving drug safety and pharmacovigilance.
The Homegrown Versus Commercial Digital Health Platform: Scalability and Other Reasons to Go with a Commercial Solution
Public cloud offerings are making homegrown digital platforms look easier and more affordable to health system CTOs and CIOs. Initial architecture and cost, however, may be the only real benefit of a do-it-yourself approach. These homegrown systems can’t scale at the level of commercial vendor systems when it comes to long-term performance and expense, leaving organizations with a potentially costly and undereffective platform for years to come.
Over his 25 years as a health system CIO, Dale Sanders, President of Technology for Health Catalyst, has observed both the tremendous value of healthcare-specific vendor platforms, as well as the shortcomings of homegrown solutions. He shares his insights in a question-and-answer session that addresses pressing issues in today’s digital healthcare market.
Historically technology and talent were primary assets used to weigh the value of M&A activity, but data is an equal pillar. Buyers (the acquiring organizations) face enormous responsibility and risk with M&A transactions. C-suite leaders have a lot to consider—enterprise-wide technology, finances, operations, facilities, talent, processes, workflows, etc.—during the due diligence process. But attention is often heavily weighted toward time-honored balance sheet and facility assets rather than next-generation assets with the long-term strategic value in the M&A process: data. The model for conducting due diligence around data involves four disciplines:
Establish the strategic objectives of the M&A with the leadership team.
Prioritize data along with the standardization of solutions and the design of a new IT organization (i.e., a co-equal effort for data, tools, and talent).
Identify the near-term data strategic priorities, stakeholders, and tools.
Assess the talent and consider creating an analytics center of excellence (ACOE) to harness organizational capabilities.
Health Catalyst Data Operating System (DOS) is a revolutionary architecture that addresses the digital and data problems confronting healthcare now and in the future. It is an analytics galaxy that encompasses data platforms, machine learning, analytics applications, and the fabric to stitch all these components together.
DOS addresses these seven critical areas of healthcare IT:
Healthcare data management and acquisition
Integrating data in mergers and acquisitions
Enabling a personal health record
Scaling existing, homegrown data warehouses
Ingesting the human health data ecosystem
Providers becoming payers
Extending the life and current value of EHR investments
This white paper illustrates these healthcare system needs detail and explains the attributes of DOS. Read how DOS is the right technology for tackling healthcare’s big issues, including big data, physician burnout, rising healthcare expenses, and the productivity backfire created by other healthcare technologies.
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.
Population health management (PHM) is in its early stages of maturity, suffering from inconsistent definitions and understanding, overhyped by vendors and ill-defined by the industry. Healthcare IT vendors are labeling themselves with this new and popular term, quite often simply re-branding their old-school, fee-for-service, and encounter-based analytic solutions. Even the analysts —KLAS, Chilmark, IDC, and others—are also having a difficult time classifying the market. In this paper, I identify and define 12 criteria that any health system will want to consider in evaluating population health management companies. The reality of the market is that there is no single vendor that can provide a complete PHM solution today. However there are a group of vendors that provide a subset of capabilities that are certainly useful for the next three years. In this paper, I discuss the criteria and try my best to share an unbiased evaluation of sample of the PHM companies in this space.
Population health strategy can borrow a lot from public health. However, health systems haven’t had to deal with patient socioeconomic issues and need to build new skills and use new data. The skills can be adapted from the public health sphere, with hospitals developing health interventions alongside law enforcement, community-based social support, etc. The most important data are patient-reported outcomes data, social determinants of health data, and activity-based costing data. With this approach, the fundamental equation for population health would be Return on Engagement, that is the clinical outcome achieved divided by the total patient investment.
While many people are looking to Big Data to solve a lot of healthcare’s data problems, Big Data won’t offer a lot of solutions for a while to come. For one, healthcare doesn’t have “Big” data; there just isn’t the volume, velocity, or variety seen in other industries such as banking where Big Data has been used successfully. For another, Big Data seems to be the answer to almost every question from cancer to Alzheimer’s, and that’s blinding us to the reality of healthcare analytics. A big way toward answering healthcare’s problems would be to improve data literacy among not only consumers, but physicians and administrators as well. Learning to ask the right questions about the data and learning how to read data correctly will get us further down the road to improvement than the latest buzzword (in this case, “Big Data”) ever will.
The number of partnerships and collaboratives in healthcare continues to climb. One of the many complications of these deals involve integrating and governing data. In fact, 100% of the 2014 Pioneer ACOs reported that they had difficulties with data integration, which had a major and negative impact on performance. Right now, data governance in healthcare is in a transitionary stage not unlike the U.S. in the 1980s. Leaders who manage the data governance in these partnerships must be like a data-savvy version of Henry Kissinger, able to bring the data of loosely affiliated organizations together for the benefit of all.
When deciding to prioritize your clinical improvement or cost reduction efforts, it’s helpful to use clinical program costs as a key input. The idea is to start with the first three or four in the first year, then work down the list. Most health systems prioritize using inpatient costs because they do not have access to outpatient data. However as accountable care and population health efforts increase, looking at costs in silos will not be sufficient. Under the leadership of Dr. Burton, our Health Catalyst team has been collecting, analyzing, and compiling a macro, industry-level view of inpatient and outpatient costs to serve as a guide for healthcare organizations who lack access to either of those views. The following ranking is the first of its kind, combining months of detailed analysis of several California health systems to show a combined total of both outpatient and inpatient costs. The results are surprising. Adding outpatient costs significantly changes the cost rankings of many of the traditional top health system care processes.
We developed a predictive analytics framework for patient care based upon concepts from airline operations. Using the idea of an aircraft turnaround time where the airline wants to put the aircraft back into operation as soon as possible, we’ve created a way to help patients headed toward poor outcomes, along with their providers, “turnaround” and get the best possible, most cost-effective outcome. For example, in a diabetes patient, we might use variables such as: age, alcohol use, annual eye/foot exam, BMI, etc. to look for patterns that might influence two outcomes: 1) Diabetic control and 2) The absence of progression toward diabetic complications. The notion of our Patient Flight Path is useful at both the conceptual level, as well as the predictive algorithm implementation level.
We sat down with Senior Vice President of Strategy, Dale Sanders, and asked him about healthcare reimbursements, risk models, and how physicians are handling these changes. Dale explains that reimbursements aren’t changing very fast. And in today’s risk models, there isn’t a lot of risk for providers or insurance companies. Good data and a strong culture around change are the best predictors of success. Federal ACOs have invested far more than they’ve recovered and few are willing to re-enroll in the ACO program unless major changes are made. As for looking at high-risk patients, most of the high-risk interventions have focused on preventable readmissions, motivated by CMS penalties. There seem to be two root categories for interventions: provider-centric (better discharge planning; scheduling follow-up visits at the time of discharge) and patient-centric (the socio-economic factors like transportation to care and lifestyle challenges). Finally, when data is introduced into a physician’s practice, most are surprised by how little they actually use evidence-based best practices.
Finding the perfect data governance environment is an elusive target. It’s important to govern to the least extent necessary in order to achieve the greatest common good. With the three data governance cultures, authoritarian, tribal, and democratic, the latter is best for a balanced, productive governance strategy.
The Triple Aim of data governance is: 1) ensuring data quality, 2) building data literacy, and 3) maximizing data exploitation for the organization’s benefit. The overall strategy should be guided by these three principles under the guidance of the data governance committee.
Data governance committees need to be sponsored at the executive board and leadership level, with supporting roles defined for data stewards, data architects, database and systems administrators, and data analysts. Data governance committees need to avoid the most common failure modes: wandering, technical overkill, political infighting, and bureaucratic red tape.
Healthcare organizations that are undergoing analytics adoption will also go through six phases of data governance including: 1) establishing the tone for becoming a data-driven organization, 2) providing access to data, 3) establishing data stewards, 4) establishing a data quality program, 5) exploiting data for the benefit of the organization, 6) the strategic acquisition of data to benefit the organization.
As U.S. healthcare moves into its next stage of evolution, the organizations that will survive and thrive will be those who most effectively acquire, analyze, and utilize their data to its fullest extent. Such is the mission of data governance.
Through a series of happy coincidences, when Dale Sanders was looking for the next move in his career, he ended up at the Cayman Islands Health Services Authority, and started on the path of an amazing story that we’ve decided to share through a documentary. You see, six months after starting as the CEO, he met Dr. Devi Shetty and his team from Narayana Health System in India. They had a unique proposition: built a 2,000-bed hospital, medical school, and long-term care facility in the Cayman Islands, based on the ground-breaking ideas that had worked in India, that could meet the need for high-quality, low-cost healthcare . In spring of 2014, Dr. Shetty and his team, in collaboration with the Cayman Islands government, Ascension Health in the U.S., and prominent businessman, Gene Thompson, opened the doors on the first phase of the project, a 200-bed hospital specializing in cardiothoracic and orthopedic surgery. The documentary describes how they are using many innovative approaches and data to drive out significant costs while maintaining high standards of quality, leading to what many are calling a truly “breakthrough” model for healthcare. Our mission at Health Catalyst is to transform healthcare, hence we wanted to share the amazing work Dr. Shetty and his team do in India, and now, in the Cayman Islands. Health Catalyst funded, produced, and directed this documentary, From the Heart, with no financial relationship and nothing to gain. We simply believe it is a story that must be told.
Disease Surveillance: Monitoring and Reacting to Outbreaks (like Ebola) with an Enterprise Data Warehouse
The current options for monitoring data to help identify disease outbreaks like Ebola are not great. These are: 1) Monitoring chief complaint/reason for admission data in ADT data streams. Although this is a real-time approach, the data is not codified and would require some degree of NLP. 2) Monitoring coded data collected in EHRs. The most precise option available, but the data is not available until after the patient encounter is closed, which would be too late in most cases. And 3) Monitoring billing data. This approach has the same problems as the two listed above, but it’s better than nothing in the absence of an EMR. All of these weaknesses can be solved with the use of a data warehouse.
Would customers go to a restaurant where they didn’t how much they owed until 90-days after finishing dinner? From workflow to customer service, see how a restaurant would operate if it were run by healthcare. Then ask yourself – why do we put up with this in healthcare if we don’t have to?
What are the hottest trends in healthcare data warehousing and analytics? Read about them from Dale Sanders, one of the industry’s foremost experts. Dale has been one of the most influential leaders in healthcare analytics and data warehousing since his earliest days in the industry and is frequently introduced as the leading authority on healthcare data warehousing and business intelligence in the United States.
Dale Sanders, SVP at Health Catalyst, gave a presentation covering healthcare analytics strategy at the recent Plante Moran executive Healthcare Summit. He covered similarities between his former career in assessing nuclear threats for the US Air Force and NSA, and his role today in advising effective use of healthcare analytics. His session covered the Healthcare Analytics Adoption Model and how organizations need to take a systematic, strategic approach to implementing new software solutions. He also covered the importance of establishing a healthcare data acquisition plan, in light of the coming patient-driven sources of data, such as wearable devices.
Meaningful use is a federal program with good intentions, but it fails to produce meaningful results for clinicians and patients. A recent study found no correlation between quality of care and observance of meaningful use requirements. Stage 1 was a much-needed prod for the industry, but it’s time to let organizations drive themselves to fully use their EMRs. The core principles of EMR utilization include guidelines around: Encounters, Medications, Problem Lists, Allergies, Orders, Progress Notes, and In Basket. Stages 2 and 3 of meaningful use should be eliminated.
Almost 90 percent of healthcare spending is associated with traditional pay-for-service contracts. But value-based purchasing encourages effective and proactive healthcare delivery. The Cayman Islands Health Services Authority started their value-based purchasing program, even agreeing to a 3 percent reduction in payments for selected diagnoses and procedures. The list of patients the system start with includes: Inpatient procedures (knee replacements, hip replaces, hip and knee replacement revisions, and hip and knee arthroscopy), Outpatient procedures (cataract removal, perinatal care, hysterectomy, and maternity), Chronic conditions (asthma, diabetes, end stage renal), and Acute conditions (upper respiratory infection). The US healthcare system should set a goal to have 80 percent of healthcare dollars going to value-based contracts by 2019.
I’ve been a CIO in various forms throughout my decades-long healthcare IT career and have always help healthcare IT vendors to high expectations. Now that I’m on the vendor side of healthcare IT, I hold our company and myself to those same high levels of expectations. Here are the ten great behaviors I expect of my vendors:
Help me compete, hire, measure, save, listen, expand, plan and innovate, migrate, prove, and evolve.
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.
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.
While information and data security is a long-standing body of practice and knowledge in corporations, data governance is less mature, especially in healthcare. As a result of this lower maturity, there is a tendency to operate in extremes, either too much governance or too little. Over time, as data and analytic maturity increases, the healthcare industry will find a natural equilibrium. In this post, Dale identifies simple practices of data governance in 7 areas: 1) balanced, lean governance, 2) data quality, 3) data access, 4) data literacy, 5) data content, 6) analytic prioritization, and 7) master data management.