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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When realized, the promise of precision medicine (to specifically tailor treatment to each individual) stands to transform healthcare for the better by delivering more effective, appropriate care. To date, to achieve precision medicine, health systems have faced financial, data management, and interoperability barriers. Current trends in healthcare, however, will give researchers and clinicians the quality and breadth of health data, biological information, and technical sophistication to overcome the challenges to achieving precision medicine.
Four notable trends in healthcare will bolster to growth of precision medicine in the coming years:
- Decision support methods harness the power of the human genome.
- Healthcare leverages big data analytics and machine learning.
- Reimbursement methods incentivize health systems to keep patients well.
- Emerging tools enable more data, more interoperability.
The Key to Healthcare Mergers and Acquisitions Success: Don’t Rip and Replace Your IT (Executive Report))
Healthcare mergers and acquisitions can involve a lot of EMRs and other IT systems. Sometimes leaders feel like they have to rip and replace these systems to fully integrate organizations. However, this is not the answer, according to Dale Sanders. This report, based upon his July 2017 webinar, outlines the importance of a data-first strategy and introduces the Health Catalyst® Data Operating System (DOS™) platform. DOS can play a critical role in facilitating IT strategy for the growing healthcare M&A landscape.
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.
How prepared are healthcare organizations to enter into value-based care? Many may not be ready. While early value-based care adopters have focused on improving and measuring quality, they’ve often overlooked steps to bear the associated financial risk. Now that health systems can enter into alternative payment models and risk-based contracts, they need to ensure that cost is as much a priority as quality.
Health systems can achieve sustainable value-based care success by optimizing the five core competencies of population health management:
- Governance that educates, engages, and energizes.
- Data transformation that addresses clinical, financial, and operational questions.
- Analytic transformation that aligns information and identifies populations.
- Payment transformation that drives long-term sustainability.
- Care transformation as a key intervention in value-based contracts.
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:
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.
Healthcare organizations seeking to achieve the Quadruple Aim (enhancing patient experience, improving population health, reducing costs, and reducing clinician and staff burnout), will reach their goals by building a rich analytics ecosystem. This environment promotes synergy between technology and highly skilled analysts and relies on full interoperability, allowing people to derive the right knowledge to transform healthcare.
Five important parts make up the healthcare analytics ecosystem:
- Must-have tools.
- People and their skills.
- Reactive, descriptive, and prescriptive analytics
- Matching technical skills to analytics work streams
Dr. John Haughom explains 5 key Deming processes that can be applied to healthcare process improvement. These include 1) quality improvement as the science of process management, 2) if you cannot measure it, you cannot improve it, 3) managed care means managing the processes of care (not managing physicians and nurses), 4) the importance of the right data in the right format at the right time in the right hands, and 5) engaging the “smart cogs” of healthcare
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.
Effective care management is essential during the first 30 days after discharge to prevent unnecessary readmission and associated costs. Care managers can follow a 10-step readmission reduction program to help patients stay on track with recovery and avoid acute care:
- Call the patient within two days of discharge.
- Assess the patient’s self-care capacity.
- Frontload homecare and ensure patient ‘touches’, if appropriate.
- Conduct a home safety evaluation.
- Order and install durable medical equipment prior to discharge.
- Order an emergency alert/medication reminder system and preprogram important phone numbers on patient’s phone.
- Implement fall prevention program, intervention, and education.
- Provide in-home education on new diagnoses or unmanaged chronic conditions.
- Connect the patient with community resources.
- Establish a best practice for follow-up phone calls after discharge.