Frequently Asked Questions

Clinical Improvement FAQs

What is the ability of Health Catalyst to stratify entire patient populations based on the types and severity of conditions and the potential opportunities for intervention such as the ability to utilize clinical indicators (rules) to identify patients by condition specific treatment opportunities?

Response: Health Catalyst’s Foundational Applications help clinical, financial, and operational teams advance down the path of understanding data in their specific areas of focus and are excellently positioned to address the needs of population management. Each Foundational Application consists of a near real-time data mart and one or more analytical application that provides advanced analytics and drill-down capabilities in an easy-to-use web and mobile accessible format. The list of applications is growing and currently includes

  • Over 30 clinical conditions (e.g., asthma, ischemic heart disease, gastroenteritis, cancer)
  • Over 20 procedures (e.g., appendectomies, spine surgeries, supracondylar fractures)

All Foundational Applications are integrated through a common architecture that allows for easy changes and customizations to make your data actionable. They are built with expandability in mind and are intended to be the team’s analytic foundation. Our architecture and application engine ease the process of adding metrics or changing existing metrics as the needs of your institutions change.

One of our Foundational Applications, Population Explorer, provides an easy-to use interface that lets users explore a wide variety of metrics across a growing list of clinically validated cohort definitions. Users can view metrics about individual patient populations, including case counts, readmission rates, charges, revenue, and length of stay, among many other metrics. Each of these metrics can be stratified by demographic information and other clinical and financial information. The tool makes it very easy to switch between different populations and provides comparative views between and among multiple populations. Population Explorer is the starting framework for identifying at-risk populations. Advanced Applications in conjunction with Health Catalyst’s improvement services are then used to develop condition-specific improvement objectives, interventions, outcomes, and metrics. 

What is the ability of Health Catalyst to stratify entire patient populations based on the types and severity of conditions and the potential opportunities for intervention such as the ability to utilize clinical indicators (rules) to identify patients by high dollar claims?

Response: The Cohort Finder tool is currently used to identify patients and encounters according to a wide variety of clinical, financial, and administrative filters. While we have not had the opportunity to work with a client who has incorporated claims data into their warehouse at the time of implementation, at least one of our customers is loading CMS claims data from the Pioneer ACO project into their warehouse. We are confident that the incorporation of a filter within Cohort Finder to identify patients who fall in a top nth percentile for claims would be an easy and valuable addition to the tool.

In addition, our KPA tool has the ability to measure various cost metrics along with variation to help select areas that warrant attention. Although we have not stratified care based on claims reimbursement within KPA, we would be open to the possibility of working with HCA to define the requirements for this enhancement and exploring the possibility of adding this to our product suite.

What is the ability of Health Catalyst to stratify entire patient populations based on the types and severity of conditions and the potential opportunities for intervention such as the ability to utilize clinical indicators (rules) to identify patients by patterns of inappropriate utilization?

Response: Inappropriate utilization of healthcare resources can also be described as waste. Industry experts estimate that the US healthcare system has an opportunity to reduce up to 30% of the cost of healthcare, which is currently attributable to waste. Health Catalyst views this waste as addressable, and we have woven the thread of waste reduction through all our applications, infrastructure, and services. The Key Process Analysis tool (KPA), discussed earlier in this response, can be used to quickly assess clinical variation across providers and locations. That variation is expressed as a severity-adjusted coefficient of variation and compares utilization within populations of patients with similar conditions. Exposing this waste through the data allows the client to eliminate it, thereby improving utilization patterns. 

What is the ability of Health Catalyst to stratify entire patient populations based on the types and severity of conditions and the potential opportunities for intervention such as the ability to utilize clinical indicators (rules) to identify patients by preventative treatment opportunities?

Response: Health Catalyst’s Population Explorer not only focuses on identifying chronically ill populations, but it also focuses on prevention strategies around each chronic condition and in general. For example, the Population Health advanced module focuses on primary care around the following common ACO, PQRS, and Meaningful Use prevention measures: 

  • Medication Monitoring (ACE/ARB/diuretic)
  • Breast Cancer Screening
  • Colorectal Cancer Screening
  • Chlamydia Screening
  • Evidence-Based Cervical Cancer Screening
  • Annual Flu Vaccine
  • Pneumonia Vaccine

What is the ability of Health Catalyst to identify “gaps in care” and any associated alerting capabilities?

Response: The Population Health advanced module was built to support the shifting payment models that place a larger emphasis around primary care. This module helps providers and healthcare systems that are taking on risk either through formal payment channels such as VBP, accountable care, capitation, etc. or informally through internal quality initiatives. Built at the core is a care coordinator workflow that identifies high utilization, ability for outreach, and risk stratification among chronically ill patients. Leveraging knowledge assets from content providers, the Population Health module identifies patients that are out of compliance with the standard of care for a given condition. Identifying those patients lets the provider intervene to fill gaps in care. 

What is Health Catalyst's ability to group patients into disease profiles for analysis and planning?

Response: Health Catalyst feels that the implementation of disease registries is critical to empowering clinical improvement in populations of patients with chronic disease. Health Catalyst delivers applications and services that accelerate the discovery, exploration, and operationalization of disease registries, typically in three phases.

First, Care Improvement Teams are empowered with a tool called Cohort Finder. Cohort Finder is an ad hoc querying application that allows non-technical users to rapidly filter and profile patient populations using clinical, financial, and administrative data.

Next, Health Catalyst’s data architects configure specific analytic applications using the population definitions developed with Cohort Finder.

Finally, Population Explorer provides an easy-to use interface that lets users explore a wide variety of metrics across a growing list of clinically validated cohort definitions. Users can view metrics about individual patient populations, including case counts, readmission rates, charges, revenue, and length of stay, among many other metrics. Each of these metrics can be stratified by demographic information and other clinical and financial information. The tool makes it very easy to switch between different populations and provides comparative views between multiple populations.

How does Health Catalyst describe ETG episode grouping methodology as well as the ability to customize grouping rules and logic?

Response: ETGs (or Episode Treatment Groups) are a way to better understand which healthcare services, typically provided within a defined window of time, can be logically grouped together into a condition-related Episode. The Health Catalyst data warehouse platform can integrate with popular third-party treatment grouper products, such as those from OptumInsight and 3M. Alternatively, customized grouping rules and logic can be developed to effectively tag ambulatory and acute services as belonging to the same episode. Those tags can then be surfaced through Health Catalyst’s applications.

How does Catalyst use patient registries to track patients with specific diseases and diagnoses to ensure they are receiving appropriate care?

Response: Health Catalyst provides a rich set of services based on a proven methodology for clinical improvement. One of the key initial steps in the Health Catalyst methodology is the definition of an accurate representation of patients impacted by the clinical process that is being optimized. Once that patient cohort is defined, the registry (or module) is built into the Health Catalyst architecture. The architecture provides data structures to hold detailed cohort definitions. It also provides data structures and technology to hold metrics related to that cohort. The metrics are customizable by a data architect and can be sourced from clinical guidelines, institutional best practices, regulatory bodies, and other sources. The metrics are displayed in a visualization layer, usually alongside a target value. The module architecture holds granular data in a way that metrics surfaced in the visualization layer always allow drill down, often to the order level. The drill-down functionality can often point to the “why” behind a piece of data, which allows physicians and other clinicians to implement appropriate interventions to improve the metric.

How does Health Catalyst measure compliance with evidence-based treatment guidelines in the delivery of primary care?

Response: Establish a primary care dashboard that provides rates and analysis indicating provider compliance to diabetes monitoring, cardiology monitoring, and prevention screening. This capability could easily be expanded to any chronic disease or procedure measured in an EMR, and it allows clinicians to monitor compliance and identify patients that need additional care.

In progress — expected benefits are better compliance, improved patient compliance tracking, improved clinician compliance management tracking, etc.

Data (Quality, Governance, Migration, Integration) FAQs

What integration does Health Catalyst have with HIM/EMR/EHR systems today?

Response: Health Catalyst has built a suite of tools that facilitate quick development of feeds from any source system. These tools have been developed to create rapid time to value in an environment where we know we will continually see data sourced from systems that we have not previously connected with. As such, we have invested huge amounts of money, time, and resources in the creation of these quick integration and data acquisition tools. For reference, our Cerner feed (including the data warehouse load) was created in just 90 days with our advanced source mart tool set, which populated a data warehouse consisting of 14 billion rows. We have reason to believe that this is the largest and fastest data warehouse ever established in healthcare from a system that we had previously never integrated with.

Our current list of source system feeds includes the following:

  • Epic
  • Cerner
  • Apollo
  • EPSi
  • TSI
  • API
  • PeopleSoft
  • Lawson
  • Press Ganey
  • NRC Picker

It should be noted that Health Catalyst’s integration and data acquisition approach renders the task of integrating new information into the data warehouse a simple and straightforward process. We list the interfaces in this section that we currently have in place but would also suggest that an additional apropos question would be related to how difficult it is to integrate data from new source systems into the warehouse. As mentioned above, a major new data load from the largest hosted Cerner customer was affected in just 90 days involving a data load of 14 billion rows of data. Catalyst feels that this statistic is as relevant as which source systems we have integrated with in the past. 

What exporting tools are offered by Health Catalyst such as exporting to other systems, warehouses or data-marts?

Response: Microsoft SQL Server 2012 provides several tools and a number of mechanisms for extracting and formatting data for export purposes. At the simplest level, SQL Server Integration Services includes an “Import and Export Wizard” that provides a simple graphical user interface to create data extractions in a number of forms, including common separated values (CSV), XML, and other textual forms. 

Another extraction option is direct connection to the database via SQL Server database connectors and extracting the desired data directly from the database for importing into another database or into applications like Power Pivot, Excel, and Access. ETL jobs can be used to extract data directly from the warehouse on an ad hoc basis or during regularly scheduled ETL processes. Health Catalyst also provides a facility within our Cohort Finder application that allows select patient information to be exported by end users into Excel spreadsheets.

The Health Catalyst EDW contains extensive metadata for each database, table, and column to simplify the extraction process.

What external sources does Catalyst integrate with?

Response: The Late-Binding Architecture of the Health Catalyst data warehouse streamlines and simplifies the process of bringing data into the warehouse. A Source Mart brings a new data source into the data warehousing platform and provides the necessary linkages with Atlas. The platform includes sophisticated tools that enable Health Catalyst or our clients to quickly develop, test, and deploy source marts. There is also a library of pre-configured Source Marts for popular clinical, financial, and ancillary systems, including the following:

  • EMR: MEDITECH, Cerner, Epic, McKesson, Siemens, Allscripts
  • Financial: Lawson – GL, Lawson – AP, Lawson – MM, PeopleSoft – GL, PeopleSoft – AP, PeopleSoft – Supply Chain, PeopleSoft Payroll, MEDITECH, McKesson
  • Human Resources: UltiPro – HR, Lawson – HR, Time Card Tracking, Kronos, API
  • Patient Satisfaction: Press Ganey, NRC Picker
  • Standard Costing: EPSi, Alliance
  • Cardiovascular: LUMEDX (Apollo)
  • Radiology: GE Healthcare, McKesson, Philips Healthcare, Fujifilm Med Sys, Agfa Healthcare, Merge Healthcare
  • Laboratory: Sunquest Lab, Cerner Labs, MEDITECH, SSC Soft Computer, McKesson

Pharmacy: MEDITECH, Cerner, McKesson, Epic, Siemens

What is Catalyst's data quality and validation processes - both for integrated tools/flags and project workflow?

RESPONSE: Some people in the data warehousing community advocate a position that involves heavy downstream cleansing of source system data. We have found that this is a risky practice that has great potential for damaging credibility with the analyst communities that the EDW is trying to serve. The danger lies in the fact that the downstream data cleansing requires extensive subject matter expertise, is prone to error, and is not transparent to the users. When an analyst community distrusts the data from the EDW, it is very hard to win that trust back. We advocate a methodology that maintains data fidelity with the source system and provides feedback to source system data stewards about data quality issues. Once those data quality issues are fixed, the corrected data automatically flows into the EDW.

We have a data validation framework that helps us maintain the fidelity between the EDW and the source system. One of the primary features compares source table row counts and EDW row counts at varying degrees of granularity to identify when a table is out of sync with the source system.

What data import and export capabilities does the Catalyst system have?

Response: Data can be imported into the EDW using traditional methods from flat-files (such as Excel) or other databases using import wizards, which are typically part of the relational database system. The Catalyst EDW includes a flat-file ETL interface for importing formatted files on a daily or on-demand basis. Data can also be exported to Excel files or other databases via secure, standard ODBC-style database drivers and connection strings.

Data Warehouse FAQs

When does normalization occur in the Health Catalyst data warehouse build?

Response: There are two uses of the term “normalization” in healthcare data management. In traditional database design, normalization is the term used to describe the process of organizing the fields and tables of a relational database to minimize redundancy and dependency. In healthcare, we use the term normalization to describe the process of rationalizing data to a common vocabulary or definition, such as Patient ID, SNOMED, CPT, LOIN, RxNorm, etc. The term can also be used to describe the process of standardizing the representation of clinical algorithms and business rules, such as calculating length of stay, provider-patient attribution, and defining cohorts of patients for inclusion and exclusion from patient registries. Catalyst has deep and successful experience in all of these definitions of the term. Our Late-Binding™ Data Warehouse methodology is unique in the industry in its ability to quickly adapt and map to the constantly changing vocabulary standards, business rules, and clinical algorithms in healthcare. The diagram below shows the two layers at which data is normalized in the Catalyst architecture.


In the Catalyst methodology, data is extracted directly from a source system and loaded into a Source Mart. The source systems include all the typical systems in healthcare, such as EMR, claims, pharmacy, costing, billing, registration, etc. In a Source Mart, we land the data in the Catalyst EDW with very little transformation or normalization. We do this purposely to maintain the fidelity and granularity of the data in the EDW as it relates to the data in the source system. We expose the data in the Source Marts to data analysts who are working in a vertical analytic area, such as lab, materials management, or radiology. This is a very important differentiator for Catalyst in contrast to other vendors. The next step in the process is normalizing the data in the Source Mart to common data types and terminologies. This occurs in Catalyst’s Late-Binding™ Bus Architecture. The final step in normalization occurs in subject-specific applications and data models.

Many vendors employ data warehousing methodologies that require all the data to undergo massive transformation and mapping into comprehensive data models before any of the data can be exposed to end users. These approaches are very costly, time consuming, and risky. They have almost no track record of success in healthcare beyond claims processing. During mass normalization (as opposed to Catalyst’s Late-Binding™, incremental normalization) of data, the process often changes the data beyond all recognition to the analysts that are accustomed to working with the source system data. As a result, the organization loses a significant amount of intellectual property that accumulated over many years in working with the source system data.

Catalyst believes that an organization will be more successful if they expose source system data largely unchanged in the Source Mart layer and undertake more ambitious normalization and data modeling in later stages when subject matter experts are engaged in building Integrated Marts and Subject Area Marts. We call this our Late-Binding™ methodology. Catalyst does not tightly bind data to a data model, vocabulary, business rule, or clinical algorithm until there is comprehensive, persistent agreement in the organization or the industry about that binding. Until then, the Catalyst methodology allows for agility and adaptability where necessary and complete standardization of data and reports where appropriate.

What is the Health Catalyst database management system used in warehousing structure?

Response: Catalyst uses Microsoft SQL Server as its DBMS. A typical deployment uses one fairly substantial server for both the DBMS and SSIS. This is only for the DBMS and ETL—other server(s) will be used for the visualization layer. Another deployment scenario employs two servers, one for the DBMS and another for SSIS. However, we don’t often recommend this because the cost is not easily justified.

What is the scalability of Catalyst's solution for handling very large amounts of data with fast query and calculation performance?

Response: The EDW is built upon a Microsoft SQL Server Platform, which is capable of handling large amounts of data and performing calculations while maintaining performance. Furthermore, fundamental to Health Catalyst’s EDW design is the concept of a Late-Binding™ Data Warehouse.

This Late-Binding™ architecture avoids unnecessary complexity that delays time to value and leads to a very fragile and inflexible data warehouse infrastructure that could not adapt to rapidly changing analytic use cases or new data content. This has proven many times more scalable and adaptable to new analytic use cases and data content than the methodologies that utilize early binding, tightly coupled data models and vocabulary management. (For more information on our Late-Binding™ warehouse, please see our website.)

The late binding of attributes allows users to adjust or modify as needed. This leads to higher performance and enables the data to be loaded into multiple visualization applications for further analysis, summary, and drill-down.

Reporting and Dashboards FAQs

What is the Catalyst user assignment logic for receiving specific dashboard content/reports and how customizable/flexible is the assignment structure?

Response: As part of Catalyst’s installation services, we will provide a data governance framework utilized by our clients, but our general approach is for access to reports and dashboards to be granted by data stewards—subject matter experts within your organization—who have an informed understanding of data access needs. (This is not always the case with Information Technology staff.) Authorization is typically role based by subject area, and it is recommended that these roles not be overly granular or restrictive. Included with the Catalyst platform are auditing tools that allow your organization to monitor not only which users are accessing the data, but also what data they are accessing. As such, we tend to take a more open approach to data warehouse access, but we encourage monitoring and auditing of that use. 

What are the interactive visualization capabilities offered for reporting function?

Response: Health Catalyst’s reporting philosophy is that those needing data should not have to rely on data analysts but should be able to obtain the information they need through self-service applications. To that end, Catalyst’s suite of applications provides interactive solutions for clinicians, operational leaders, and staff across your entire organization. QlikView is the visualization tool Catalyst believes provides the most robust solution, allowing users to view summary information (e.g., performance on regulatory measures) or drill down to patient-level data (e.g., opportunities for improvement on the same regulatory measures). The choice of visualization tool ultimately resides with each client.

What is the ability to publish/send user created reports to other users?

Response: Health Catalyst’s Discovery Applications and Advanced Applications are based on integrated clinical and financial source data, which include a starter set of tools to accelerate each team, including evidence-­based clinical cohort definitions and starter visualizations (including clinical or operations dashboards, balanced scorecards, etc.) that include core clinical and operational metrics for measurement and improvement. Because Catalyst’s open platform is visualization-tool agnostic, these starter sets are delivered in the client's preferred visualization tool, and reports and dashboards can be modified and created as desired. The tools we develop can be used by different users based on the implemented organization tree. Although most of our clients haven’t frequently requested this functionality, Catalyst has the ability to leverage an organization tree that will enable row-level security at a broad deployment level.  

Service, Support, Training FAQs

How is training handled? Is training different based on the type of user?

Response: Catalyst has two training tracks: 

  • Technical training (for both technical and analytics resources)
  • General training (for technical, clinical, and administrative project resources)

Catalyst has determined at a high level that it is better to cross-train the technical and analytics team than it is to separate training sessions. Catalyst has found that data architects are more successful in their core data architect roles when they understand and can even perform basic analytics functions. Likewise, Catalyst has found that analysts are more successful when they understand the underlying data structures implemented by the data architects. All training is included in the statement of work pricing.

Catalyst has two types of training:

  • Formal content training
  • Individual, side-by-side, on-the-job shadowing and coaching

Catalyst has a formalized set of training courses for each audience. These are grouped and tracked by phases in order to ensure greater client success. At the end of each phase, an assessment is done by Catalyst to determine the skill and knowledge base of the client’s technical and clinical resources. 

Catalyst’s offering includes comprehensive training through Catalyst University, and its data architects will provide hands-on training to CHNw’s team. This hands-on training often takes place during Phase 2 of the implementation (Clinical Engagement Implementation phase).

The following are the key technical Catalyst courses grouped by phase:

Technical and Analyst Training



Phase 1


Agile Development Principles


Expectation Setting


Lean Tools and Principles


Understanding Variation


Clinical Content System (or Clinical Tool Training)


Tool Training (or Analytics Tool Training)


Source Mart Master


Adaptive Data Warehouse Guiding Principles


Technical infrastructure Principles


Master Reference and Master Data Management


Move to Production Process and Checklist


Security and Data Stewardship Principles


QlikView Development

Phase 2



Discovery visualizations


Process Metric visualizations


Summary Dashboard Visualizations


Understanding Variation (advanced)


Statistical Process Control


Cohort Technical Definition


SAM Metadata Management


Visualization Best Practices


Visualization Style Guide 203, 204, 205, 225, 226


Change Control


Knowledge Asset Management

Phase 3


Setting up a KPA


Agile Leadership


Data Storytelling


The following are the key general Catalyst courses grouped by phase:

General Training (technical, clinical, administrative roles)



Phase 1


Challenges in Healthcare / History of Medicine


Expectation Setting


Lean Tools and Principles


Anatomy of Healthcare Delivery


Analytics System


Deployment System


Content System


Phase 2 SAM Overview


Phase 2 Roles/Responsibilities

Phase 2


Complex Challenges in Healthcare


Introduction to Metrics


Healthcare Financial Costs, Savings, and Cost Recovery


Data Quality


Waste Reduction


Fingerprinting and Soliciting Input


Implementation Plan Best Practices

Phase 3


Lean Facilitation


Deployment Acceleration


Critical Communication

What is your ongoing support after the implementation – both operational and technical? Are these services included in your license and ongoing maintenance fees?

Response: Health Catalyst provides all support resources (technical and clinical) necessary during implementation. After implementation, the client will have the option to continue support and maintenance. An annual maintenance and support fee covers both operational and technical support.

Does Health Catalyst provide efficient contract work for custom report and analytic modeling development?

Response: Health Catalyst provides all support resources (technical and clinical) necessary during implementation. After implementation, the client will have the option to continue support and maintenance. An annual maintenance and support fee covers both operational and technical support.

Strategic Solutions FAQs

What capabilities does Health Catalyst offer regarding Health Information Exchange?

Response: The Health Catalyst product offerings deliver excellent data analysis capabilities as well as robust tools to drive clinical improvement. They are not designed or built for the Health Information Exchange requirements outlined in this section, but they are built to easily consume and interact with data that is made available via HIEs. While Health Catalyst does have extensive internal capabilities to reconcile patient identity and standardize both clinical and financial vocabularies, these facilities are not intended for enterprise use outside of our solution. We recognize the need for enterprise-class solutions to the master data management challenge across the HCA network and would expect that any HIE solution chosen under the requirements of this RFI would include those capabilities. We are fully prepared to work with any HIE solution selected to add value on top of what you may achieve from that solution alone. 

There are several ways in which the Health Catalyst platform can be deployed to leverage one or more health information exchanges. Because of the geographic concentration of HCA facilities in various divisions, it may be desirable to establish a clinically integrated network per division. Each division may therefore have its own HIE, perhaps with several HIE vendors represented across the HCA enterprise. If there are several clinically integrated networks established, an instance of the Health Catalyst infrastructure could be deployed to support each network. Alternatively, it may be desirable to have all the clinically integrated networks share a single instance of the Health Catalyst infrastructure. The Catalyst platform could support either approach, and we look forward to further discussions of your business requirements and technical preferences to determine the best architecture to support HCA’s needs.

A foundational purpose of a health information exchange is to acquire and aggregate data from across a clinically integrated network and feed those data into a clinical intelligence platform. Health Catalyst supports multiple data acquisition methodologies that could be applied to HIE data acquisition, including database extraction and message-based streams. For large data sets that are updated frequently, database extraction is typically the preferred method. It provides high throughput and ensures access to the complete set of available data. For HIEs that aggregate and store data for a longitudinal record, this approach may be appropriate. Health Catalyst can also support message streams in a variety of formats, including HL7 and Consolidated CDA, which may be better suited for some implementations.

In addition to acquiring data from an HIE, we offer several integration approaches between the clinical intelligence platform and two key components of the HIE, the patient identification service and the vocabulary or terminology service.

Once the HIE has established a patient identification methodology and service, there are two ways for those patient identifiers to flow into the Health Catalyst platform. One method is to have the source systems integrate with the MPI, ensuring that the MPI identifier for the patient is stored alongside the source system identifier for the patient. When data from the source system flows into the Health Catalyst platform, the MPI identifier will be present and will be used to relate data from other source systems to the same patient. A different method of integrating the MPI into the data warehouse would be useful when the source system is not yet integrated with the MPI. Under this approach, when data from a source system come into the Health Catalyst platform, the platform would query the MPI to obtain the proper patient identifier, which would then be persisted in the data warehouse. These methods are not mutually exclusive, and depending on the capabilities of the MPI service, there may be other approaches available. 

Technology FAQs

What hosting environment does Health Catalyst use?

Response: The client can choose between a local self-hosted environment or a virtual private cloud option. Health Catalyst’s cloud-based product has been newly introduced and is being rolled out to a small number of new clients. Health Catalyst has received considerable interest in our new cloud solution and will aggressively roll out the cloud option to customers who prefer a Catalyst-managed EDW solution.

Where is data stored?

Response: For clients choosing an on-premise solution, all data are stored in the local data center and is under control of the client at all times. After extensive evaluations, Health Catalyst has selected Amazon Web Services (AWS) as its hosting provider. Health Catalyst uses AWS dedicated, reserved instances. Dedicated, reserved instances are hardware and software instances that are used by a single client. Nothing is shared. The EDW is not part of a multitenant database; it is a dedicated database running on a dedicated hardware instance. Health Catalyst dedicated instances are deployed in Amazon Virtual Private Clouds (VPCs), which function as an extension of the client’s data center. A hardware gateway is placed within the data center and manages an encrypted tunnel pair between the data center and the VPC for secure data transmission from the local source systems to the cloud-based EDW. No public Internet ports are exposed in the VPC, thereby forcing all communication between the client’s source systems and the cloud EDW through the secured encrypted channels. All data is stored in instances resident within data centers physically located in the continental United States.

What data base is the system built on?

Response: The Health Catalyst Data Warehouse Platform is built on Microsoft SQL Server 2012 Enterprise Edition. Extract, Transform, and Load (ETL) processes are built, scheduled, and executed with Microsoft SQL Server Integration Services (SSIS). MS SQL Server Management Studio (SSMS) is used for day-to-day management of the SQL Server environment. SQL Server Reporting Services (SSRS) are also part of the MS SQL Server product family and can be leveraged for simple reporting tasks.

Health Catalyst is currently investigating alternative database management systems, including Teradata Database, Microsoft Parallel Data Warehouse (PDW), and Oracle Database 11g for very large implementations requiring advanced, high-performance features like high availability (HA), extremely high I/O rates, massively parallel processing, and advanced clustering capabilities available only in these specialty products.

To date, Microsoft SQL Server 2012 has scaled to meet the demands of Health Catalyst customers, but we acknowledge that very large, single-instance implementations are likely to require the aforementioned advanced relational database management system features currently unavailable in MS SQL Server 2012 Enterprise Edition. We are prepared to adapt to satisfy those requirements.

What hardware and software arrangements are offered in the Health Catalyst architecture?

Response: The illustration below details the list of hardware and software components in a basic Health Catalyst deployment. These components serve only as a starting point for a new deployment. Many factors need to be considered to properly design and configure the production and development environments for each deployment.

The Health Catalyst Data Warehouse is deployed on enterprise-class commodity hardware from respected vendors like HP and IBM with familiar components like Intel Processors and SAS disk drives. The operating system is Microsoft Windows Server 2012, and the database system is typically Microsoft Windows SQL Server 2012 Enterprise Edition. A storage area network (SAN) connected to a switched fiber channel network is recommended to meet the high I/O requirements of the warehouse.

The Visualization Server is an optional component of the Catalyst warehouse deployment. The Health Catalyst Platform is vendor neutral with respect to business intelligence, analytics, and reporting tools. In the illustration below, the QlikView Business Intelligence Platform is included as an example of a BI platform that has been proven in several deployments to work very well with the Health Catalyst Platform. 

Figure - Basic Catalyst Deployment
basic catalyst deployment

The illustration below represents a more complete Health Catalyst Deployment. A test environment is used for the development of new analytical applications and visualizations. The production environment is physically separate from the development and testing environment to ensure the best possible performance for users as well as isolation from side effects introduced during the development process. It is possible to share the storage area network, but we leave that decision to local IT resources and their governing policies.

Figure - Full Deployment
advanced catalyst deployment

Do all tools offered by Health Catalyst use the same security, metadata (both structural and descriptive), portal integration, query engine and have the same look and feel?

Response: The same security mechanisms are used throughout Catalyst applications and management tools. Metadata, captured and maintained in the Catalyst Metadata subsystem, is used across all Catalyst applications. Catalyst applications are metadata-driven from the local metadata repository. All Catalyst applications and tools use the query engine of the underlying, commercial database engine. The Catalyst systems do not employ on linked servers or run heterogeneous queries across multiple database systems. Catalyst applications of the same application family have the same look and feel. For example, system management applications, targeted to system administrators, have a common look and feel. Visualizations, used by clinical staff and similar end users, have a common look and feel that varies from administration applications. The Catalyst user experience is built around specific personas, so differing personas have a customized user experience with a consistent interface.

What Disaster Recovery capabilities are offered?

Response: Health Catalyst Source Marts are mildly transformed copies of data contained in the client’s source systems. The client’s source systems are the systems of record for data in the EDW. Therefore, the Health Catalyst EDW can be fully re-created from data in the source systems. Health Catalyst recommends backing up the metadata system, administration tables, and data created by Health Catalyst’s Instant Data Entry Application (IDEA) application. For faster EDW recovery, clients may optionally back up the Source Marts and Subject Area Marts and then restore them from backup copies in the event of a catastrophic failure. Once restored from backups, the Source Marts can be brought up to date by running the nightly ETL processes. In the vast majority of cases, the Catalyst EDW is not a critical system required for immediate patient care, so system availability and disaster recovery requirements are usually much less demanding than direct patient care (bedside) systems like the EMR. 

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