Does PowerChart Use Automated Measure Calculations?

An intelligent calculator to determine the likelihood of automated clinical and operational measure calculation within the Cerner PowerChart EHR.

Automation Likelihood Calculator

Likelihood Analysis

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Contributing Factors:

Please select options from the dropdowns above to see the analysis.

Chart illustrating the calculated likelihood of automation.

What is Automated Measure Calculation in PowerChart?

The question, “does PowerChart use automated measure calculations,” refers to the capability of the Cerner PowerChart Electronic Health Record (EHR) to automatically compute clinical, financial, or operational metrics without manual human intervention. Instead of a person having to review a patient’s chart and abstract data into a spreadsheet (a process known as manual abstraction), the system itself runs logic to determine if a patient meets the criteria for a specific measure. This is crucial for healthcare quality reporting, clinical decision support, and operational efficiency.

PowerChart, as a comprehensive EHR, has extensive capabilities for automation. However, the successful automation of any given measure is not a simple “yes” or “no.” It heavily depends on how data is entered, how the measure is defined, and how the system is configured. For example, electronic Clinical Quality Measures (eCQMs) are specifically designed for electronic extraction and calculation from EHRs like PowerChart.

The “Formula” for Automation Likelihood

There is no single mathematical formula to determine if PowerChart uses automated measure calculations for a specific need. Instead, it’s a logical assessment based on several key variables. The likelihood increases when data is structured and standardized, and the measure’s logic is straightforward. This calculator uses a weighted score based on the factors you select above.

Variables affecting automation potential. Units are conceptual scores.

Variable	Meaning	Unit	Typical Range (Score)
Measure Type	The level of standardization and formal specification of the measure.	Standardization Score	0 (Custom) to 3 (eCQM)
Primary Data Source	How the required data is captured in the EHR. Structured data is machine-readable.	Structure Score	0 (Unstructured) to 3 (Discrete)
Data Complexity	The number and variety of data sources required for the calculation.	Complexity Score	0 (Complex) to 2 (Simple)

For more on quality measures, see our guide on Understanding eCQMs.

Practical Examples

Example 1: High Automation Likelihood

A hospital wants to track CMS130, “Colorectal Cancer Screening,” an official eCQM.

• Inputs:
  • Measure Type: eCQM
  • Primary Data Source: Discrete, Coded Data (Procedure codes for colonoscopy, CPT codes for FOBT)
  • Data Complexity: Single Data Point (evidence of one valid screening test)
• Result: Very High Likelihood. PowerChart is designed to automate these calculations using its reporting and rules engines when certified.

Example 2: Low Automation Likelihood

A research coordinator wants to find patients who received “motivational counseling for smoking cessation” where the counseling details are only documented in a free-text progress note.

• Inputs:
  • Measure Type: Custom / Locally-Developed Measure
  • Primary Data Source: Unstructured / Free-text Notes
  • Data Complexity: Single Data Point (but hidden in text)
• Result: Very Low Likelihood. Standard PowerChart tools cannot reliably parse free text for complex concepts without advanced (and often separately licensed) Natural Language Processing (NLP) tools. This usually requires manual chart review. For more information, read about EHR Data Abstraction techniques.

How to Use This PowerChart Automation Calculator

This tool helps you assess whether your metric is a good candidate for automation within PowerChart. Follow these steps:

1. Select Measure Type: Choose the category that best fits your measure. Standardized measures like eCQMs are the easiest to automate.
2. Identify Data Source: Determine where the data needed for the measure is stored in PowerChart. Is it a lab value (discrete), a flowsheet row (structured), or buried in a note (unstructured)?
3. Assess Complexity: Consider if the calculation requires a single piece of data (e.g., last A1c value) or multiple pieces from different parts of the chart (e.g., diagnosis, medication, and lab value).
4. Review Results: The calculator will provide a likelihood score and explain the contributing factors. Use this to understand the technical feasibility and what might be needed to enable automation. For an overview, check our resource on Cerner PowerChart Reporting.

Key Factors That Affect Automated Calculations in PowerChart

Beyond the inputs in the calculator, several environmental factors determine if PowerChart uses automated measure calculations effectively:

• 1. System Build and Configuration: The specific tools, rules, and reporting packages licensed and configured by the healthcare organization are paramount.
• 2. Use of Structured Documentation: Clinician adherence to using structured tools like PowerForms and flowsheets over free-texting is critical for data capture.
• 3. Data Governance and Standardization: Consistent use of standard terminologies (SNOMED, LOINC, RxNorm) ensures data can be reliably queried.
• 4. Natural Language Processing (NLP): For measures requiring data from unstructured notes, the availability and sophistication of an NLP engine is the deciding factor.
• 5. Reporting Tool Proficiency: The skill of the analysts and report writers using tools like Discern Analytics, Business Objects, or other reporting platforms is key to building the actual calculations.
• 6. Interoperability: If data must come from an external system, the quality and reliability of the interface directly impact automation success. This is a core part of the Clinical Quality Measures framework.

Frequently Asked Questions (FAQ)

1. Does PowerChart automate all eCQMs out of the box?

No. While PowerChart is certified for eCQM calculation, it requires significant local configuration, workflow validation, and testing to ensure accuracy for specific programs like MIPS.

2. Can PowerChart calculate measures from free-text or dictated notes?

Not with its base reporting tools. This requires advanced Natural Language Processing (NLP) solutions, which may be licensed separately, to parse unstructured text and convert it to structured data.

3. What is the difference between a calculated measure and a rule-based alert?

A calculated measure is typically run retrospectively on a population of patients for reporting. A rule-based alert (like a Discern Expert alert) often runs in real-time for a single patient to provide point-of-care clinical decision support.

4. Who is responsible for building these automated calculations?

This is typically a collaborative effort between clinical informaticists (who define the logic), application analysts (who build in PowerChart), and report writers/data analysts (who extract the data). Explore our article on Clinical Informatics Jobs.

5. Can measures be calculated in real-time?

Some can, particularly if they are built as Discern Expert rules for clinical decision support. However, complex population-level measures are usually calculated in batches by reporting tools.

6. What are “structured data” and “unstructured data”?

Structured data is information in discrete, predictable fields, like a dropdown list, a lab value, or an ICD-10 code. Unstructured data is free-form, like a narrative progress note.

7. Does the PowerChart Touch mobile app affect automated calculations?

Indirectly. If clinicians use PowerChart Touch to enter data into structured fields, that data becomes available for automated calculations. However, the calculations themselves are performed on the server, not on the mobile device.

8. Where can I find the official specifications for eCQMs?

The official source for eCQM specifications is the Electronic Clinical Quality Improvement (eCQI) Resource Center provided by HealthIT.gov and CMS.

Related Tools and Internal Resources

Explore these other resources to deepen your understanding of EHR data and analytics: