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Early- or Late-binding Approaches to Healthcare Data Warehousing: Which Is Better for You?

Most industries use enterprise data warehouses (EDWs) to create meaningful analytics on their operations and processes. Healthcare has long struggled with implementing and maintaining EDWs. One reason for this is that a lot of the data healthcare uses is unstructured, meaning there are few to no restrictions on it. And this unstructured data can exist in several systems within the organization. Additionally, health systems must pull data from many sources, such as EMRs, financial systems, and patient satisfaction data. The early-binding approach to data warehousing makes the binding decisions early in the process and, thus, lacks the agility healthcare needs to respond to ever-changing business rules and requirements. This approach can also take a long time to implement. Late-binding data warehousing has a much faster time-to-value and allows users to create analytics based on what-if scenarios. Plus, it can change to reflect the always-moving world of healthcare analytics needs.

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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.

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Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growing Health Data Demands

Smartphone applications, home monitoring equipment, genomic sequencing, and social determinants of health are adding significantly to the scope of healthcare data, creating new challenges for health systems in data management and storage. Traditional on-premises data warehouses, however, don’t have the capacity or capabilities to support this new era of bigger healthcare data. Organizations must add more secure, scalable, elastic, and analytically agile cloud-based, open-platform data solutions that leverage analytics as a service (AaaS). Moving toward cloud hosting will help health systems avoid the five common challenges of on-premises data warehouses:

  1. Predicting future demand is difficult.
  2. Infrastructure scaling is lumpy and inelastic.
  3. Security risk mitigation is a major investment.
  4. Data architectures limit flexibility and are resource intensive.
  5. Analytics expertise is misallocated.

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Comparing the Three Major Approaches to Healthcare Data Warehousing: A Deep Dive Review (White Paper)

The task to improve healthcare presents a significant challenge to providers, health systems, and payers. But according to the Institute for Healthcare Improvement, if health systems focus on achieving the objectives of the Triple Aim, they will be able to meet the ongoing government mandates to improve care. A key component for meeting the Triple Aim will require the ability to overcome the current data warehouse challenges the healthcare industry faces. Because of constantly changing business rules and definitions, health systems need to choose a data warehouse that’s able to bind volatile and nonvolatile data at different stages rather than the early binding approach that’s inherent with traditional data warehouses. The best type of healthcare data warehouse should offer a late-binding approach, which will provide the following critical characteristics: data modeling flexibility, data flexibility, a record of changes saved, an iterative approach, and granular security.

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Academic Medical Centers: A Triple Threat Approach to Leveraging Healthcare Analytics

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.

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Agnostic Analytics Solutions vs. EHRs: Six Reasons EHRs Can’t Deliver True Healthcare Interoperability

As enterprisewide analytics demands grow across healthcare, health systems that rely on EHRs from major vendors are hitting limitations in their analytics capabilities. EHR vendors have responded with custom and point-solution tools, but these tend to generate more complications (e.g., multiple data stores and disjointed solutions) than analytics interoperability. To get value out of existing EHRs while also evolving towards more mature analytics, health systems must partner with an analytics vendor that provides an enterprise data management and analytics platform as well as deep improvement implementation experience. Vendor tools and expertise will help organizations leverage their EHRs to meet population health management and value-based payment goals, as well as pursue some of today’s top healthcare strategic goals:

  1. Growth.
  2. Innovation.
  3. Digitization.

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Interoperability in Healthcare Data: A Life-Saving Advantage

When health system clinicians make care decisions based on their organization’s EHR data alone, they’re only using a small portion of patient health information. Additional data sources—such as health information exchanges (HIEs) and patient-generated and -reported data—round out the full picture of an individual’s health and healthcare needs. This comprehensive insight enables critical, and sometimes life-saving, treatment and health management choices. To leverage the data from beyond the four walls of a health system and combine it with clinical, financial, and operational EHR data, organizations need an interoperable platform approach to health data. The Health Catalyst® Data Operating System (DOS™), for example, combines, manages, and leverages disparate forms of health data for a complete view of the patient and more accurate insights into the best care decisions.

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The Top Five 2019 Healthcare Trends

Bobbi Brown, MBA, and Stephen Grossbart, PhD have analyzed the biggest changes in the healthcare industry and 2018 and forecasted the trends to watch for in 2019. This report, based on their January 2019, covers the biggest 2019 healthcare trends, including the following:

  • The business of healthcare including new market entrants, business models and shifting strategies to stay competitive.
  • Increased consumer demand for more transparency
  • Continuous quality and cost control monitoring across populations.
  • CMS proposals to push ACOs into two-sided risk models.
  • Fewer process measures but more quality outcomes scrutiny for providers.

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The Digitization of Healthcare: Why the Right Approach Matters and Five Steps to Get There

While many industries are leveraging digital transformation to accelerate their productivity and quality, healthcare ranks among the least digitized sectors. Healthcare data is largely incomplete when it comes to fully representing a patient’s health and doesn’t adequately support diagnoses and treatment, risk prediction, and long-term health care plans. But even with the obvious urgency for increased healthcare digitization, the industry must raise this trajectory with sensitivity to the impacts on clinicians and patients. The right digital strategy will not only aim for more comprehensive information on patient health, but also leverage data to empower and engage the people involved. Health systems can follow five guidelines to digitize in a sustainable, impactful way:

  1. Achieve and maintain clinician and patient engagement.
  2. Adopt a modern commercial digital platform.
  3. Digitize the assets (the patients) and the processes.
  4. Understand the importance of data to drive AI insights.
  5. Prioritize data volume.

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The Six Biggest Problems With Homegrown Healthcare Analytics Platforms

Most healthcare systems have been building, improving, and maintaining proprietary healthcare analytics platforms since the early 2000s and have invested heavily in the people and resources required to do so. As the demands of today’s healthcare environment continue to increase, it’s becoming more difficult for analytic teams to keep up. This article deals with the six biggest problems to maintaining a homegrown healthcare analytic platform today:

  1. Inability to keep pace with analytic demands.
  2. Difficult to support and scale for the future.
  3. Difficulty finding and keeping talent.
  4. Use of point solutions to fill gaps.
  5. Analytic teams must also support third-party vendors and affiliated groups.
  6. Difficulty keeping abreast of rapidly changing regulatory requirements.

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The Healthcare Data Warehouse: Lessons from the First 20 Years

Twenty years after Intermountain Healthcare launched its enterprise data warehouse in 1998, industry leaders are looking at what they did right, what they’d do differently, and what the future holds for healthcare data and analytics. While early successes (such as a hiring framework of social, domain, and technical skills; lightweight data governance; and late-binding architecture) continue to hold their value, advanced analytics and technology and innovation in diagnosis and treatment are reshaping the capabilities of and demands on the healthcare data warehouse. Present-day and future healthcare IT leaders will have to revisit approaches to data warehousing people, processes, and technology to understand how they can improve, continue to adapt, and fully leverage emerging opportunities.

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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.

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Clinical Data Management: 3 Improvement Strategies

Most health systems suffer from data clutter and efficiency problems. As a result, analysts spend most of their time searching for data, not performing high value work.  There are three steps that can help you address your data management issues: 1) find all your dispersed analysts in the organization, 2) assess your analytics risks and challenges, 3) champion the creation of an EDW as the foundation for clinical data management.

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Cloud-Based Open-Platform Data Solutions: The Best Way to Meet Today’s Growing Health Data Demands

Smartphone applications, home monitoring equipment, genomic sequencing, and social determinants of health are adding significantly to the scope of healthcare data, creating new challenges for health systems in data management and storage. Traditional on-premises data warehouses, however, don’t have the capacity or capabilities to support this new era of bigger healthcare data. Organizations must add more secure, scalable, elastic, and analytically agile cloud-based, open-platform data solutions that leverage analytics as a service (AaaS). Moving toward cloud hosting will help health systems avoid the five common challenges of on-premises data warehouses:

  1. Predicting future demand is difficult.
  2. Infrastructure scaling is lumpy and inelastic.
  3. Security risk mitigation is a major investment.
  4. Data architectures limit flexibility and are resource intensive.
  5. Analytics expertise is misallocated.

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Data Warehousing in Healthcare: A Guide to Success

Looking for a way to share his extensive experience with data warehousing in healthcare, in 2002 Dale Sanders wrote what many consider to be the “EDW Bible.” It’s a document with guidance that, if followed, will drive value and utilization from a data warehouse. We’ve made that report available now.

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Database vs Data Warehouse: A Comparative Review

What are the differences between a database and a data warehouse? A database is any collection of data organized for storage, accessibility, and retrieval. A data warehouse is a type of database the integrates copies of transaction data from disparate source systems and provisions them for analytical use. The important distinction is that data warehouses are designed to handle analytics required for improving quality and costs in the new healthcare environment. A transactional database, like an EHR, doesn’t lend itself to analytics.

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Episode Analytics Now Mission Critical as Outcomes Meet Incomes: Partners HealthCare Paves Volume-To-Value Path With Late-Binding Data Warehouse

In this reprint from Microsoft, Dennis Schmuland, MD, FAAFP (Chief Health Strategy Officer, Microsoft US Health & Life Sciences), sits down with Sree Chaguturu, MD (Vice President and Chief Population Health Officer, Partners HealthCare) to learn how Partners HealthCare has prepared for the tipping point of value-based care.

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Healthcare Data Warehouse Models Explained

Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.

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The Best Solution for Declining Medicare Reimbursements

I am one of the brave souls who takes the time to read the report issued each spring by the Medicare Payment Advisory Commission (Medpac). The report shows the numbers of Medicare beneficiaries and claims are growing; healthcare organizations are increasingly losing money on Medicare; payment increases certainly will not keep pace with declining margins; and Medicare policies will continue to incentivize quality and push providers to assume more risk. But the report also reveals that some healthcare organizations—referred to as “relatively efficient”—are making money from Medicare with an average 2 percent margin. How do you become one of these organizations? And how do you target and counter Medicare trends that impact your business?

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Healthcare Analytics Platform: DOS Delivers the 7 Essential Components

The Data Operating System (DOS™) is a vast data and analytics ecosystem whose laser focus is to rapidly and efficiently improve outcomes across every healthcare domain. DOS is a cornerstone in the foundation for building the future of healthcare analytics. This white paper from Imran Qureshi details the seven capabilities of DOS that combine to unlock data for healthcare improvement:

  1. Acquire
  2. Organize
  3. Standardize
  4. Analyze
  5. Deliver
  6. Orchestrate
  7. Extend
These seven components will reveal how DOS is a data-first system that can extract value from healthcare data and allow leadership and analytics teams to fully develop the insights necessary for health system transformation.

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