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Which Epic analytics tool fits your question?

Start with the question and the action that follows its answer. SlicerDicer explores populations within available models; Reporting Workbench supports operational lists and actions. Clarity and Caboodle supply different reporting structures. Databasin implements analysis across Epic and other business systems when the answer depends on those records together.

Databasin · November 4, 2026

Selected by reporting purpose, available data, freshness and the work needed to maintain the answer.

Starting points for common questions

OptionBest forSetup
SlicerDicerExploring a population in an available modelModels, access and training at the institution.
Reporting WorkbenchActing on an operational record listA suitable template, criteria and permissions.
ClarityBuilding detailed reports with custom calculationsApproved access and specialist query development.
CaboodleReusing an implemented reporting subject areaCoverage and measures validated locally.
DatabasinAnswering questions across clinical and business recordsOur team implements connections, definitions and reports.

Five routes to Epic answers

SlicerDicer

Best for

People exploring patient populations within the models available at their institution.

Setup

Local data models and permissions determine which questions a user can explore.

In SlicerDicer, users narrow a population and explore its trends through an available model. They can vary a question without commissioning a custom report each time. The model's fields, measures and groupings determine which variations it can answer. For an encounter question, establish the encounters included and the date that sets the reporting period. Users can then change their selections without changing the underlying definition. Start here when that model contains the evidence the question needs.

Strengths

  • Population exploration without writing a custom SQL report.
  • Familiar access within the institution's Epic environment.
  • Reusable models support consistent questions across users.

Limits

  • Available models determine the scope of exploration.
  • Local permissions govern access to sensitive detail.
  • Refresh timing determines the periods available for review.

Reporting Workbench

Best for

Staff who need operational lists and authorized actions inside Epic.

Setup

Templates establish the records, criteria and display elements available to a report.

Reporting Workbench supports operational reports within the Epic workflow. It is useful when the answer leads directly to work on a list of records. The report's template and the user's permissions determine what can be searched and what action follows. A recurring operational list and a large historical analysis place different demands on a system. When a report needs extensive joins or long periods, the reporting team should assess whether a warehouse extract better suits that workload. Keep the report's purpose visible during that decision.

Strengths

  • Reports sit close to the operational workflow.
  • Templates support repeatable record selection and display.
  • Authorized actions can follow from report results.

Limits

  • Templates bound available search criteria and columns.
  • Large historical workloads need separate performance assessment.
  • Operational access does not authorize every secondary use.

Clarity

Best for

Reporting specialists building detailed analyses with custom joins and calculations.

Setup

Analysts need approved access and knowledge of the relevant clinical data model.

Clarity gives reporting specialists relational records for custom joins and calculations. This detail is useful when an existing report cannot express the question. Before reusing a query, the analyst needs to resolve the local fields and relationships on which its answer depends. Keep the query and reporting definition together. A changed flowsheet build or status can alter the population even if the report's title stays familiar. Comparing the affected records helps the reporting team establish whether the series still means the same thing.

Strengths

  • Detailed source records support specialist analytical work.
  • SQL supports explicit joins and reusable calculations.
  • Reporting workloads can use extracted operational data.

Limits

  • Local documentation practices affect interpretation of fields.
  • Complex relationships require specialist modeling and validation.
  • Extract schedules determine the data's available freshness.

Caboodle

Best for

Teams producing repeated analysis from implemented Epic reporting subject areas.

Setup

Confirm the local model contains the fields and history required by the report.

Caboodle organizes records into structures used for analytical queries. An implemented subject area can save preparation when it already contains the report's relationships. The institution's actual coverage determines how much of the proposed measure is ready to use. Vanderbilt describes a Caboodle implementation containing much of its Clarity data and links to external sources. Another institution's coverage can differ. Inspect the local records and relationships when deciding whether a report can use the existing model.

Strengths

  • Reporting structures organize relationships for analytical queries.
  • Implemented subject areas support repeated use across reports.
  • Local models can incorporate external data relationships.

Limits

  • Model coverage differs between institutional implementations.
  • Custom fields may require additional modeling work.
  • A reporting model still needs agreed business definitions.

Databasin

Best for

Teams connecting clinical activity with staffing, finance or research operations.

Setup

Our team implements source connections, shared definitions, access controls and the resulting reports.

Databasin combines Epic records with other business data and implements the definitions, reports and workflows that use them. For a staffing review, that means aligning clinical activity with workforce periods and organizational units. People can then examine the same measures in Data Apps or ask questions in plain English. The engagement includes the work around the software: defining the outcome, building it, helping people use it and arranging ongoing support. This route fits when the unresolved work extends across systems and teams. Existing Epic reports can continue to serve the questions they already answer.

Strengths

  • Connects clinical questions with other business records.
  • Team implements shared definitions and governed reporting.
  • Rollout and support accompany the technical implementation.

Limits

  • Source access must cover the proposed analytical use.
  • Cross-system measures require compatible dates and definitions.
  • The engagement establishes the implementation and support scope.

How these options were selected

These options were selected for different reporting jobs within an Epic organization. The criteria below express the emphasis of this guide. A useful selection starts with whether the answer can be produced, then considers how people will use it and who will maintain it. The five entries include interfaces, reporting stores and an implemented platform because a real investigation may use more than one of them.

Question and data coverage

35%

Use in everyday work

25%

Required data freshness

20%

Continuing operation and ownership

20%

Epic · Healthcare Intelligence · Accessed 2026

Vanderbilt Clinical Informatics Center · Epic Data Resources · Accessed 2026

Medical College of Wisconsin · SlicerDicer Overview · Accessed 2026

What happens next

We would like to hear where your team wants to make progress with data and AI. We can discuss a project you have in mind or explore where Databasin could help with the work.

Book a demo

What happens next

  1. Book a demo

    Book a conversation about your organization and the work you want to improve. Questions about where to start are welcome.

  2. Explore the opportunity

    We show a working example that answers your business questions, then discuss what it would take to deliver it for your team.

  3. Get the first result

    After agreeing the first use case, we implement it with your team. We train the people using it and measure how it performs.

FAQ

Why do patient and encounter counts differ?

A patient count and an encounter count answer different questions. One person can have several encounters during the period. The report should identify its unit of analysis and show how repeat visits, canceled encounters and excluded records affect the total. This distinction matters before comparing results from two tools.

Can a population count become a research extract?

The count is a starting point for identifying a population. A record-level extract also needs a route for exporting the selected fields under the approved research use. The institution's research data or reporting team can establish that route and implement the required selection and delivery.

How should a report show data freshness?

Show the source cutoff beside the last successful refresh. A run completed today may still use yesterday's extract. When a required input is missing, mark the report incomplete and identify the gap. The recipient can then see why the report is not ready for the intended review.

Who should have access to record-level detail?

Access should follow the recipient's approved work. Someone reviewing a group total may need less detail than someone investigating an individual record. Apply the same distinction to the underlying data and exports. Revisit those permissions when the report gains a new audience.

What belongs in a report's data dictionary?

The dictionary should explain how a source record enters a reported total. Record the measure's meaning, unit, dates and exclusions, then the mappings and missing-value rules used in its calculation. A developer can use those details to locate the reports affected by a source change.

What happens when a local Epic build changes?

Test the records and results affected by the build change. A query may still run successfully after a field changes meaning. Revise the mapping or calculation where needed, retaining the earlier version with the reporting periods that used it. This preserves a way to explain older results.

Does reporting access also approve a research project?

The institution establishes the approvals for a proposed study. Reporting permission alone does not resolve its population, identifying detail or data handling. Once the research use is authorized, the data team can apply those conditions to the extract and to the route through which researchers receive it.

When can an older report be retired?

Retire it after the replacement covers the required decisions and its recipients can use it. Reconcile the results, explain intentional differences and confirm that scheduled deliveries have moved. Keep an accessible record of the former definition when people will need to interpret older reporting periods.

Explore more

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Discuss your Epic reporting needs

We can discuss the questions your reporting tools leave unanswered and how Epic data connects with the rest of your organization. A demo can explore the reporting, definitions and implementation that would help your people use the answers.

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