6.3 Executing, Piloting & Measuring CDI Project Outcomes

AHIMA Certified Documentation Integrity Practitioner CDIP

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1 Chapter 1: Authoritative Coding Resources & Conventions

1.1 Official Coding Resources: Scope, Authority & Research 1.2 General Coding Conventions, Punctuation & Instructional Notes 1.3 Chapter-Specific and Setting-Specific Coding Guidelines

2 Chapter 2: Principal & Secondary Diagnosis Determination

2.1 UHDDS Definitions & Principal Diagnosis Selection 2.2 Two or More Diagnoses Equally Meeting Criteria 2.3 Secondary Diagnosis Reporting Standards & POA Status

3 Chapter 3: Procedure Coding & Sequencing Standards

3.1 ICD-10-PCS Root Operations & Inpatient Procedure Coding 3.2 Principal Procedure Selection & Surgical Sequencing 3.3 Applying Official References to Ambiguous Records

4 Chapter 4: Inpatient Prospective Payment & DRG Grouping

4.1 MS-DRG Logic, CC/MCC Exclusions & Severity Levels 4.2 Working vs. Final DRG Assignment & Optimization vs. Maximization 4.3 CDI-Coding Reconciliation & Annual Regulatory Updates

5 Chapter 5: Enterprise CDI Promotion & Program Value

5.1 Articulating CDI Value to C-Suite & Hospital Leadership 5.2 Organizational Models, Inpatient/Outpatient Scope & Staffing 5.3 Cross-Functional Collaboration: HIM, Quality & Case Management

6 Chapter 6: Cross-Functional Alignment & Improvement Projects

6.1 Partnering with Nursing, Pharmacy & Allied Health Professionals 6.2 Documentation Improvement Project Identification & Scoping 6.3 Executing, Piloting & Measuring CDI Project Outcomes

7 Chapter 7: Physician Champion Engagement & Clinical Advocacy

7.1 Selection, Role & Credibility of the CDI Physician Champion 7.2 Peer-to-Peer Engagement, Coaching & Overcoming Resistance 7.3 Developing Standard CDI Operational Policies & Procedures

8 Chapter 8: Compliant Policy Architecture & Query Governance

8.1 AHIMA/ACDIS Practice Brief Standards in Policy Construction 8.2 Medical Record Integration of Provider Query Responses 8.3 Policies for Electronic, Paper, and Verbal Query Communications

9 Chapter 9: Query Escalation & Professional Communication

9.1 Chain of Command & Protocols for Unanswered Queries 9.2 Conflict Resolution & Professional Feedback Mechanisms 9.3 Assessing Stakeholder Learning Needs & Specialty Curricula

10 Chapter 10: Educational Delivery & Broader Healthcare Impacts

10.1 Delivery Methods: Microlearning, Case Studies & Grand Rounds 10.2 Documentation Impacts on Clinical Research & Epidemiology 10.3 Documentation Impacts on Case Management, LOS & Reimbursement

11 Chapter 11: Clinical Record Synthesis & Encounter Trajectory

11.1 Synthesizing Provider Notes, Consults & Progress Trajectory 11.2 Interpreting Diagnostic Imaging, Pathology & Labs 11.3 Resolving Conflicting, Ambiguous & Copy-Forward Entries

12 Chapter 12: Advanced Clinical Concepts & Review Prioritization

12.1 Pharmacotherapy, Clinical Trajectories & Disease Correlations 12.2 Inpatient Review Prioritization: High-Risk & High-Volume 12.3 Payer Prioritization, Length of Stay & Re-Review Cadence

13 Chapter 13: Clinical Indicator Mapping & Gap Recognition

13.1 Systematic Identification of Documentation Gaps 13.2 Sepsis Definitions, SEP-1, and Coding 13.3 AKI Criteria, Tubular Injury, and CKD Staging

14 Chapter 14: Diagnostic Precision in High-Impact Conditions

14.1 Respiratory Failure: Acuity, Evidence, and Sequencing 14.2 Heart Failure Phenotype, Acuity, and Hypertensive Combinations 14.3 Encephalopathy, Delirium, and Acute Cognitive Change

15 Chapter 15: Clinical Validation & Complication Discrepancies

15.1 Malnutrition Assessment, Severity, and Provider Documentation 15.2 Principles of a Compliant, Nonleading Query 15.3 Open-Ended, Multiple-Choice, and Yes/No Queries

16 Chapter 16: Query Mechanics & Direct Provider Collaboration

16.1 Electronic Query Systems, Templates, and Technology Oversight 16.2 Collaborating With the Appropriate Query Recipient 16.3 Capturing and Clarifying Query Responses

17 Chapter 17: Quality Indicators & Post-Discharge Clarification

17.1 Present on Admission Clarification and Selected HAC Rules 17.2 Clarifying Acuity, Cause, Site and Severity 17.3 Post-Discharge Query Opportunities and Limits 17.4 Managing Post-Discharge Query Workflows

18 Chapter 18: CDI Operational Dashboards & Quality Auditing

18.1 Departmental Metrics: Definitions, Calculations and Dashboard Use 18.2 Internal CDI Quality Audits and Reviewer Calibration 18.3 Query Trends: Understanding the Process and Provider Experience

19 Chapter 19: Benchmarking, Productivity & Data-Driven Workflow

19.1 Provider Documentation Benchmarking 19.2 External Benchmarking: CMI, Severity, Mortality and Length of Stay 19.3 Evaluating CDI Professional Performance 19.4 Using Data to Design CDI Workflow and Staffing

20 Chapter 20: Ethical CDI Standards & Regulatory Frameworks

20.1 Applying AHIMA Ethics to CDI Decisions 20.2 Responding to Financial Pressure and Upcoding Risks 20.3 Maintaining Competence and Professional Integrity

21 Chapter 21: Regulatory Compliance & Enforcement Landscapes

21.1 False Claims, Overpayments and Referral Laws 21.2 Regulatory Agencies, Review Contractors and Appeals 21.3 EHR Templates, Copied Text and Record Integrity

22 Chapter 22: Technology Risks & Second-Level Review Protocols

22.1 CAC, NLP and AI: Context, Oversight and Performance 22.2 Selecting Cases for Second-Level Review 22.3 Conducting and Closing a Second-Level Review

23 Chapter 23: Clinical Validation Queries & Risk Mitigation

23.1 Clinical Validation and Coding Clarification 23.2 Clinical Validation Queries and Denial Evidence 23.3 Detecting Leading, Biased and Unsupported Queries

24 Chapter 24: Noncompliant Query Remediation & Query Lifecycle Management

24.1 Query Remediation and Corrective Action 24.2 Query Timeframes, Aging and Measurement 24.3 Query Records, Retention and Evidence Preservation

6.3 Executing, Piloting & Measuring CDI Project Outcomes

Key Takeaways Explain This Topic AI

Use PDSA to test a prediction, compare actual results, learn, and plan the next cycle; DMAIC structures definition, measurement, cause analysis, improvement, and sustainment.

Match the intervention to the verified cause and keep templates and queries specific to the clinical record.

Combine outcome, process, and balancing measures; response or agreement alone does not establish appropriate documentation.

A run chart displays time-ordered data and may use a median; a control chart has method-specific statistical limits. Neither proves the cause of a change.

Adapt, implement, or stop based on accuracy, feasibility, unintended effects, and results under varied conditions; assign owners and monitoring for sustainment.

Last updated: October 2026

Execute a CDI project as a testable change

A charter states the problem and intended result. Execution turns the proposed solution into a process that people can try, measure, and improve. Keep the clinical purpose visible: more accurate, supported documentation with a workable process. A larger query count or a favorable payment shift cannot substitute for that result.

Choose a method appropriate to the project and the organization's improvement program. PDSA and DMAIC are useful structures, not competing national CDI requirements. A small test can reveal a broken notification step quickly; a complex recurring process problem may require more extensive measurement and analysis. Neither a fixed 90-day pilot nor a particular baseline duration guarantees a valid conclusion.

Apply PDSA to a specific uncertainty

IHI's Model for Improvement connects the aim, the evidence of improvement, and the proposed change with iterative Plan-Do-Study-Act testing. Each cycle should answer a practical question. Early tests can be small, followed by revisions and tests under different conditions before implementation.

Continue the illustrative nutrition-assessment project from section 6.2. The first uncertainty is whether a new notification reaches the responsible provider without creating duplicate requests.

PDSA step Local test design

Plan Try the notification for five eligible assessments on one unit. Predict that at least four reach the correct provider by the next scheduled review. Define “reaches” as a recorded acknowledgment, identify the collector, and record duplicate messages and staff time. These are test assumptions, not national standards.

Do Carry out the test. Record actual acknowledgments, routing failures, unexpected problems, and deviations from the plan.

Study Compare the findings with the prediction. Four of five acknowledgments may meet the prediction, while three duplicate messages expose a workflow problem. Review the resulting records for appropriateness as well as message delivery.

Act Revise the routing rule and test again. A promising result may justify a larger test under another shift or covering-provider arrangement. An unsafe or ineffective change may need redesign or abandonment.

Meeting one prediction does not require immediate hospital-wide rollout. Conversely, an unsuccessful test still provides useful information. Record what was learned and what the next cycle will test. Repeated testing under varied conditions helps reveal dependencies that a receptive volunteer group may not encounter.

Use DMAIC for a recurring process problem

ASQ describes DMAIC as Define, Measure, Analyze, Improve, and Control. In a CDI workflow, Define establishes the scope and users' needs; Measure establishes reliable baseline observations; Analyze investigates causes; Improve tests and refines changes; and Control establishes ownership, monitoring, and a response plan to sustain the improved process.

For repeated misrouted queries, map the route from reviewer to responsible provider, including coverage changes and handoffs. A cause-and-effect diagram can organize possible contributors such as unclear assignment rules, missing roster updates, and interface failures. These are hypotheses to investigate. Interview the people performing the steps and check actual routing records before attributing every delay to provider resistance.

A Pareto chart ranks observed categories by frequency or another stated quantity. For example, 30 reviewed routing failures might include 15 outdated rosters, nine ambiguous service assignments, and six interface errors. The first category represents 50% of the failures, not an assumed 80%. A frequently discussed “80/20” pattern is not a result to impose on every dataset. Define the categories, handle multiple causes consistently, and use the actual counts when prioritizing a test.

Match the intervention to the cause

Use education when the verified gap concerns knowledge or interpretation. Use a workflow change when the problem is that an assessment cannot be found or a question reaches the wrong person. A new educational presentation will not repair an interface that drops messages. An EHR prompt should make relevant information and the clinical question easier to address; it should not preselect a diagnosis or copy an unsupported condition into each encounter.

Prepare the users for the test. Identify who owns the updated procedure, how covering clinicians receive questions, how staff report defects, and who can pause the change. Clinical, coding, compliance, and technology reviewers should examine the parts within their responsibilities. Query wording still needs the individual record's relevant facts and reasonable response options. A diagnosis-specific template cannot make every patient meet that diagnosis.

Measure the result, the work, and unintended effects

IHI distinguishes outcome, process, and balancing measures. Apply all three to the aim rather than equating a busy program with an effective one:

Measure type Illustrative CDI measure Interpretation

Outcome Unresolved documentation discrepancies per eligible audited record Addresses the project's intended documentation result; audit criteria and population must remain comparable

Process Eligible assessments with the notification step completed as designed Shows whether the tested workflow is being carried out; does not prove the diagnosis or query is appropriate

Balancing Duplicate messages, provider time, inappropriate queries, or delayed discharge attributable to the new process Checks whether improvement in one area creates another problem

For query response, define whether the denominator is all issued queries or those whose response window has elapsed, and how closed, withdrawn, and duplicate queries are handled. Report unresolved cases rather than silently excluding them. Agreement, response speed, and coverage can be useful process information; high values do not independently establish nonleading wording or clinical support.

Some teams call an early operational measure a leading indicator and a later result a lagging indicator. Availability depends on the data and workflow. A documentation audit may be performed during the pilot; a payer denial outcome may require extended follow-up. Do not postpone all quality assessment until discharge or assume that every outcome arrives months later. Use timely review to identify unsupported diagnoses or misleading templates while the test is still running.

Distinguish a run chart from a control chart

A run chart places observations in time order. Label the measure and interval, keep definitions consistent, and annotate intervention dates and relevant external events. IHI's run-chart instructions include a median line with at least ten observations. A median provides a reference for interpreting patterns; it is not an upper or lower control limit.

A statistical control chart adds limits calculated using a method appropriate to the data and chart type. NIST describes three-sigma limits as common, with interpretation depending on distribution and estimation assumptions. A count, proportion with changing denominators, and continuous elapsed time do not all call for the same calculation. Work with qualified quality or analytic staff to select the method and interpretation rules before using it for decisions.

A point or pattern signaling unusual variation warrants investigation. It does not prove that the CDI intervention caused the change. Changes in case mix, audit practice, staffing, or data extraction may also matter. A pair of before-and-after averages can conceal substantial variation; show the underlying sequence and denominators. Avoid labeling every favorable fluctuation “statistically significant” without an appropriate analysis.

Decide whether to adapt, implement, or stop

Suppose the pilot's query-agreement rate is 94%, but an audit finds a template that steers clinicians toward one diagnosis despite weak or contradictory evidence. Pause or restrict the defective process as appropriate, examine affected records under the organization's correction procedures, revise the template and training, and retest. Do not preserve the template because its agreement rate exceeds a target. A provider's agreement does not cure a leading query, and a denial increase alone does not prove which defect caused it.

For a promising pilot, compare the outcome, process, and balancing findings with the aim and predictions. Check unresolved cases, staff feedback, and whether clinical and coding support remain sound. A small, uncontrolled pilot can establish feasibility and guide a next test; it does not automatically establish causation or predict results in every service line. Test the workflow with different coverage patterns, users, and conditions before broader implementation.

Sustainment needs an accountable owner, an approved procedure, maintained training and technology, a defined monitoring schedule, and an action when performance deteriorates. Continue checking documentation accuracy, not just whether a preferred phrase appears without a query. New staff, guidance, code versions, or service changes may require revision and another test. Where the project forms part of hospital QAPI, preserve the reasons, measures, progress, and follow-up evidence required by the program.

Primary references

IHI Model for Improvement , testing changes , and establishing measures .

ASQ DMAIC .

IHI Run Chart instructions and NIST control-chart overview .

42 CFR 482.21 , QAPI monitoring and project documentation.

2026 ACDIS/AHIMA compliant query practice brief .