Showing contents for:

Analytics

Health Catalyst Recommends

Analytics - Recently Added

Short on time? We've picked the best content for you to start with.

Improving Strategic Engagement for Healthcare CIOs with Five Key Questions

A healthcare CIO’s role can demand such an intense focus on technology that IT leaders may struggle to find natural opportunities to engage with their C-suite peers in non-technical conversations. To bridge the gap, healthcare CIOs can answer five fundamental questions to better align their programs with organizational strategic goals and guide IT services to their full potential:

  1. Whom do we serve?
  2. What services do we provide?
  3. How do we know we are doing a great job?
  4. How do we provide the services?
  5. How do we organize?

Read More
My Folder

Analytics - Additional Content

Spend time reading content for you

How to Achieve Your Clinical Data Analytics Goals

Healthcare organizations know that they need to an effective clinical data analytics strategy to improve and survive in today’s challenging environment. In order to make these necessary improvements, healthcare leaders need to establish clear goals for their clinical data analytics initiatives. Achieving these goals requires clinical teams to clearly identify problems and plan for how to achieve them. This article walks improvement teams through sometimes confusing process of identifying problems, setting clear, achievable goals, and common pitfalls along the way. Topics covered include:

  • Six categories of clinical data.
  • Three types of goals: outcome, process, and balance.
  • How to write an outcome goal.
  • Internal vs. External Benchmarks.
  • Mitigation strategies.
  • Getting clinical buy-in.

Read More
My Folder

What Healthcare Analysts Can Learn About Data Analytics From the World of Surfing

It might sound surprising, but the world of surfing just might hold key observations about the world of healthcare analytics. After watching the Pipeline Masters in Oahu, John Wadsworth, Technical Operations VP at Health Catalyst, took away three key principles from the world of surfing that are important for healthcare analysts:

  1. Understand the Changing Environment.
  2. Know When to Say No, So You Can Say Yes to the Right Opportunity.
  3. Get Good at Positioning.
This article also offers insights on moving from reactive to prescriptive analytics, the top five technical skills data analysts need, and a four step model for problem solving.

Read More
My Folder

Three Principles for Making Healthcare Data Analytics Actionable

Data is everywhere. But without a plan to extract meaning from data and turn insights into action, data can’t impact outcomes. Generating value from data takes work, but it can be done. To create compelling data insights that promote action, health systems can follow three guiding principles for actionable healthcare data analytics as well as hire analysts with seven important skills. Three principles form the foundation for actionable healthcare data analytics:

  1. Balance investments.
  2. Hire generalists over specialists.
  3. Develop a team that’s highly aligned and loosely coupled.

Read More
My Folder

Healthcare NLP: Four Essentials to Make the Most of Unstructured Data

Many health systems are eager to embrace the capability of natural language processing (NLP) to access the vast patient insights recorded as unstructured text in clinical notes and records. Many healthcare data and analytics teams, however, aren’t experienced in or prepared for the unique challenges of working with text and, specifically, don’t have the knowledge to transform unstructured text into a usable format for NLP. Data engineers can follow four need-to-know principles to meet and overcome the challenges of making unstructured text available for advanced NLP analysis:

  1. Text is bigger and more complex.
  2. Text comes from different data sources.
  3. Text is stored in multiple areas.
  4. Text user documentation patterns matter.

Read More
My Folder

The Number One Secret of Highly Effective Healthcare Data Analysts

Data-driven quality improvement is propelling healthcare transformation. The ability to strategically leverage healthcare data is essential, making highly effective data analysts more valuable than ever. So, what attributes differentiate a good data analyst from a great analyst? Stephen Covey’s well-known book “The 7 Habits of Highly Effective People,” has long had far-reaching impacts in the business world. These same principles are relevant today and applicable in the world of healthcare analytics. Learn how Covey’s second habit, “Begin With the End in Mind,” drives great healthcare data analysts.

Read More
My Folder

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.

Read More
My Folder

Employer Health Plans: Keys to Lowering Cost, Boosting Benefits

Employers that offer robust employee health plans at affordable costs are more likely to attract and retain a great workforce. Healthcare, however, is often a top expense for organizations, making balancing attractive benefits with attractive costs a complex undertaking. Employers need a deep understanding of employee populations and opportunities to manage health plan costs without sacrificing quality. An analytics-driven approach to employee population health management gives employers insight into two key steps to lower healthcare costs and enhance benefits:

  1. Manage easily fixed cost issues.
  2. Use healthcare cost savings to fund expanded benefits.

Read More
My Folder

Transforming Healthcare Analytics: Five Critical Steps

By committing to transforming healthcare analytics, organizations can eventually save hundreds of millions of dollars (depending on their size) and achieve comprehensive outcomes improvement. The transformation helps organizations achieve the analytics efficiency needed to navigate the complex healthcare landscape of technology, regulatory, and financial challenges and the challenges of value-based care. To achieve analytics transformation and ROI within a short timeframe, organizations can follow five phases to become data driven:

  1. Establish a data-driven culture.
  2. Acquire and access data.
  3. Establish data stewardship.
  4. Establish data quality.
  5. Spread data use.

Read More
My Folder

Advanced Analytics Holds the Key to Achieve the Triple Aim and Survive Value-based Purchasing

Every hospital and health system has to juggle significant IT needs with a limited budget. In the middle of these demands and possibilities, hospital executives have to prioritize and decide which technology solutions are the most critical to the health of their organization. I call these most critical IT solutions “survival software.” Advanced clinical analytics solutions are the survival software of the near future, as they really hold the key to achieving the triple aim and survive value-based purchasing.

Read More
My Folder

Five Lessons for Building Adaptive Healthcare Data Models that Support Innovation

Healthcare data models are the backbone of innovation in healthcare, without which many new technologies may never come to fruition, so it’s important to build models that focus on relevant content and specific use cases. Health Catalyst has been continuously refining its approach to building concise yet adaptive healthcare data models for years. Because of our experience, we’ve learned five key lessons when it comes to building healthcare data models:

  1. Focus on relevant content.
  2. Externally validate the model.
  3. Commit to providing vital documentation.
  4. Prioritize long-term planning.
  5. Automate data profiling.
These lessons are essential to apply when building adaptive healthcare data models (and their corresponding methodologies, tools, and best practices) given the prominent role they play in fueling the technologies designed to solve healthcare’s toughest problems.

Read More
My Folder

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.

Read More
My Folder

The Healthcare Analytics Ecosystem: A Must-Have in Today’s 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:

  1. Must-have tools.
  2. People and their skills.
  3. Reactive, descriptive, and prescriptive analytics.
  4. Matching technical skills to analytics work streams.
  5. Interoperability.

Read More
My Folder

A Behind-the-Scenes Look at Healthcare IT Analyst Rankings and Reports: What You Should Know

Healthcare leaders often turn to healthcare IT analyst rankings and reports for information that drives vendor-related decision making. Knowing the key differences between several notable healthcare and cross-industry IT analysts—what methodologies they employ to gather data, their missions and goals (ranking vs. consulting), and how much of their own opinions they interject (unbiased vs. opinionated)—will help healthcare leaders be more educated consumers of the reports and rankings that saturate healthcare. This article provides a high-level overview of the key differences between several healthcare IT analysts:

  • KLAS Research (ranking focus)
  • Black Book Rankings (ranking focus)
  • Chilmark Research (ranking and consulting focus)
  • Advisory Board (consulting focus)
It also looks at the most notable cross-industry IT analysts that apply a healthcare-specific lens to their findings:
  • Gartner
  • International Data Corporation
  • Frost & Sullivan
Healthcare leaders with the ability to interpret these rankings and reports to extract the information they need, will make them more effective decision makers.

Read More
My Folder

How to Evaluate a Clinical Analytics Vendor: A Checklist

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.

Read More
My Folder

Chilmark Report Studies the 2017 Healthcare Analytics Market Trends and Vendors

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.

Read More
My Folder

Closed-Loop Analytics Approach: Making Healthcare Data Actionable

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:

  1. Data must be transformed from its raw, obscure form into actionable insights.
  2. 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.

Read More
My Folder