Study guide
6.2 Documentation Improvement Project Identification & Scoping
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
6.2 Documentation Improvement Project Identification & Scoping
Key Takeaways Explain This Topic AI
Treat dashboard differences as signals for investigation; CMI, DRG distributions, mortality ratios, and denials do not independently identify the cause.
Define the cohort, numerator, denominator, period, source, version, and outcome follow-up before comparing rates.
Choose targeted or probability sampling to match the question, and do not extrapolate a denied-only sample to all discharges.
Write a charter with an audited problem, measurable local aim, scope, owners, intervention, and outcome, process, and balancing measures.
Prioritize verified clinical and documentation risks, preserve all appropriate diagnostic outcomes, and distinguish financial planning from unsupported reporting.
Last updated: October 2026
Select a project by establishing the problem
A promising CDI project begins with a question that matters to patient care, record integrity, or an important workflow. A dashboard can show where to investigate; a record review establishes what is happening. Separate three decisions: whether the signal is reliable, what explains it, and which change the organization can test. This prevents a revenue or severity metric from becoming a diagnosis quota before anyone has examined the underlying cases.
For Medicare-participating hospitals, 42 CFR 482.21 requires a hospital-wide, data-driven quality assessment and performance improvement program. Its priorities include high-risk, high-volume, or problem-prone areas, with attention to incidence, prevalence, severity, and effects on outcomes, safety, and quality. Hospitals must document their projects, reasons, and measurable progress. This supports connecting CDI work to the hospital's quality priorities. It does not prescribe one CDI charter template or a national minimum query-agreement percentage.
Interpret signals as competing hypotheses
Consider the following examples before choosing a diagnosis-specific initiative:
Signal Questions to investigate Evidence needed before selecting a change
Lower service-line CMI than a comparator Did services, patient mix, payer population, grouper version, or coding completeness differ? Comparable cohort and version definitions, then record-level review of supported diagnoses and procedures
High query volume Are cases more complex, notes unclear, reviewers using different thresholds, or queries duplicative? Query indications, unique-case counts, appropriateness audit, and workflow map
High proportion of a DRG without a CC/MCC Are the patients clinically different, are supported conditions missing, or are existing codes/grouping misunderstood? Actual clinical records, final codes, current grouper logic, and comparable peer definitions
Clinical validation denials Is the issue clinical support, query design, documentation, code assignment, payer application, or several causes? Denial rationale, disputed record, coding source, appeal result, and payer-specific denominator
Unfavorable quality result Is there a care problem, a data problem, a cohort change, or a model/application issue? The specific measure specification, eligible cases, timing, exclusions, and clinical quality review
CMI is a summary of assigned DRG relative weights. A difference does not by itself distinguish a documentation problem from a change in services or patient complexity. Similarly, a high query-agreement rate can coexist with leading questions or unsupported diagnoses. A defensible project identifies a specific defect through an audit rather than treating an indicator as its own explanation.
Read mortality and length-of-stay comparisons carefully
For a measure defined as observed deaths divided by expected deaths, an O/E value of 1.48 means observed deaths are 1.48 times the model's expected count for the stated cohort and period. Interpretation also depends on the model, sample size, uncertainty, and data quality. It does not prove that documentation caused the result or establish a universal national threshold for every mortality program.
Risk adjustment is measure-specific. Expected outcomes may incorporate demographics, clinical factors, timing, and other variables specified by the model. They are not universally calculated entirely from secondary diagnoses marked POA Y. Adding a diagnosis need not change a given model's expectation. Clinical review may identify either missing supported risk information or genuine care concerns; both deserve an honest account. Do not target additional comorbidities simply to make an unfavorable result appear better.
Length-of-stay comparisons also require matching definitions. A service's arithmetic average LOS and a DRG's geometric mean LOS are different statistics. A difference can suggest cases to review, but it does not establish missing complications or an individual patient's appropriate discharge date. Separate medical complexity, clinical delays, placement barriers, operational delays, and data errors. Payment and transfer rules require their own application, as discussed in the grouping chapter.
Validate the data before auditing the clinical hypothesis
Write a short measure dictionary before comparing results. Specify the population, setting, dates, unit of analysis, numerator, denominator, exclusions, data source, and code/model version. State whether a denial count represents claims, diagnosis lines, unique encounters, or all appeal events. Deduplicate the same case when the intended unit is a unique encounter.
Check whether the denominator has had enough follow-up for the outcome. A recent discharge cohort may have few denials simply because payers have not completed review. Compare cohorts with a consistent opportunity for an outcome to appear, and keep pending decisions distinct from upheld or overturned denials. Record extraction changes, missing data, and changes in payer mix. Otherwise, a reporting change may look like a clinical improvement.
Choose a sample that answers the question
A targeted review is useful for investigating an unusual subgroup, such as repeatedly denied records. A probability sample can estimate how often a defect occurs in a defined population, subject to sampling uncertainty and sound execution. These designs answer different questions. A set containing only denied claims cannot establish the defect rate among all discharges. A convenience sample from the most cooperative providers may miss problems elsewhere.
Build an audit instrument with explicit criteria and sources. Separate a missing provider explanation, an unsupported diagnosis, a coding error, and a compliant record. Reviewers should apply the same definitions and reconcile disagreements about the instrument before interpreting trends. Include cases without the suspected defect so that the team can test alternative explanations. A project is better scoped when the team can say what would disprove its initial assumption.
Turn findings into a charter
Suppose a hospital suspects that nutrition assessments are not being reconciled with provider documentation. In an illustrative local audit, reviewers examine 100 eligible adult inpatient records selected from a defined discharge period. They find 20 with an unresolved discrepancy after applying the same review criteria. This is a 20% observed defect rate in that audit. The finding concerns the sampled population and stated method; it does not establish that all 20 patients had malnutrition or that a particular severity code was omitted.
A useful charter could contain:
Problem: unresolved nutrition-assessment/provider-documentation discrepancies in 20 of 100 audited eligible records.
Aim: reduce the observed unresolved-discrepancy rate to 10% or less in the defined follow-up audit by the end of the next quarter, using the same eligibility and adjudication rules.
Scope: the selected adult medical unit and the handoff from dietitian assessment to provider review; other units remain outside the initial test.
Team: an accountable sponsor, project lead, dietitian, diagnosing-provider representative, CDI, coding, quality, and an analyst; involve other affected staff as needed.
Proposed change: test a reliable notification and review process that makes the assessment visible and supports an appropriate clarification when needed.
Measures: unresolved-discrepancy rate, completion of the notification step, provider workload, and audit of the resulting documentation and queries.
Decision: review the test findings with the sponsor and decide what to revise before broader implementation.
These numbers and roles are a worked local design, not AHIMA requirements. A follow-up audit of 100 eligible records with 10 unresolved discrepancies would be 10%, an absolute decrease of 10 percentage points and a relative decrease of 50% from 20%. That arithmetic describes the samples; a causal or statistical conclusion requires an appropriate analysis of variation, comparability, and other changes.
The aim should permit all clinically appropriate resolutions: a supported diagnosis and degree, a different explanation, no additional condition, or an inability to determine. “Obtain severe malnutrition in 90% of queried records” would distort that purpose. Financial effects may be estimated in program planning, with explicit assumptions and costs. Including financial information in a charter is not itself a False Claims Act violation. The compliance concern is whether actual documentation, queries, coding, and claims are truthful and supported, not whether the project has a budget.
Prioritize and authorize the test
Compare candidate projects on clinical risk, verified defect frequency, affected volume, equity concerns, feasibility, staff burden, and available resources. A low-volume issue with serious safety consequences may deserve attention before a higher-volume administrative inconvenience. Conversely, a large apparent financial opportunity with an unverified clinical premise needs investigation before implementation.
Present the sponsor with the evidence, uncertainty, scope, proposed measures, owner, and resources. Obtain the applicable local clinical, compliance, technology, and operational approvals for the change being tested. A charter makes the work accountable; it does not authorize a template to insert a diagnosis or bypass provider judgment. The next section explains how to learn from the test and decide whether to adapt, implement, or stop it.
Primary references
42 CFR 482.21 , hospital QAPI requirements; project example above is locally designed.
CMS Measures Management System risk-adjustment overview .
IHI Model for Improvement , aims and measures.
2026 ACDIS/AHIMA compliant query practice brief .
AHIMA Standards of Ethical Coding, 2016 version , truthful, supported reporting.
DOJ False Claims Act overview , liability for false claims and related conduct.