7.5 Quality and Process Improvement

Performance Improvement Models

Performance improvement (PI) is the continuous study and adjustment of processes to raise quality and efficiency. The dominant model is the iterative PDSA cycle — also called PDCA:

• Plan — identify the problem and design a change.
• Do — pilot the change on a small scale.
• Study (Check) — measure results against the prediction.
• Act — adopt, adapt, or abandon the change, then repeat the cycle.

Because it is iterative, PDSA drives continuous quality improvement (CQI) rather than a one-time fix.

FOCUS-PDCA prefixes the cycle with a structured front end: Find a process to improve, Organize a team that knows it, Clarify current knowledge, Understand variation/causes, Select the improvement — then run PDCA. It is widely tested in HIM because it forces problem definition before action.

Lean, Six Sigma, and DMAIC

Two industrial methodologies appear on the RHIT exam:

• Lean focuses on eliminating waste (the eight wastes — defects, overproduction, waiting, etc.) and maximizing value to the customer. Tools include value-stream mapping and 5S workplace organization. Lean speeds up flow — useful for shortening chart-completion or ROI turnaround.
• Six Sigma focuses on reducing variation and defects (target: 3.4 defects per million opportunities) using statistics and the DMAIC cycle:

Lean Six Sigma blends both. Root cause analysis (RCA) — often triggered by a sentinel event under Joint Commission requirements — digs past symptoms to the true cause using tools like the 5 Whys and the fishbone diagram. Contrast with failure mode and effects analysis (FMEA), which is proactive — anticipating failures before they occur.

The Seven Basic Quality Tools

PI teams display and analyze data with the seven basic QI tools:

The Pareto chart (use it to target the few causes producing most coding errors) and the fishbone (brainstorm causes of a billing delay) are the most heavily tested.

QI vs. QA and Benchmarking

Quality improvement (QI) is proactive and continuous — redesigning processes to prevent problems. Quality assurance (QA) is retrospective inspection — checking output against a standard after the fact (e.g., auditing 5% of coded charts for accuracy). The field has shifted from QA to CQI.

Benchmarking compares your performance to a standard — internal (your own trend), competitive (peer hospitals), or best-practice — to set realistic improvement targets, such as a coding-accuracy or DNFB (discharged-not-final-billed) goal.

Oversight, Accreditation, and the QI Structure

Quality improvement in healthcare is not optional — it is required by accreditors and payers. The Joint Commission and the CMS Conditions of Participation (CoP) mandate an organization-wide performance-improvement program, and the Joint Commission requires a root cause analysis after a sentinel event plus proactive FMEA on at least one high-risk process. HIM data feeds these programs: accurate coded data, mortality and infection rates, and core-measure abstraction all originate in the record. The HIM professional is therefore a key supplier of the data that drives QI.

QI activity is organized through a PI plan, interdisciplinary teams, and oversight committees, with results reported up to administration and the governing board. Data displays make findings actionable — a dashboard of key indicators (coding accuracy, chart-completion turnaround, query response rate) lets leaders monitor at a glance.

Run Charts, Common vs. Special Cause, and Targets

A run chart plots a measure over time to reveal trends; a control chart adds statistical upper and lower control limits to separate common-cause variation (inherent, expected noise) from special-cause variation (a specific, assignable problem to investigate). Reacting to common-cause noise as if it were a real problem — tampering — usually makes things worse, a frequently tested distinction. To reduce common-cause variation you must redesign the process itself; to address special cause you investigate and remove the specific assignable factor.

Improvement targets should be SMART (specific, measurable, achievable, relevant, time-bound) and grounded in benchmarks rather than guesswork. Together these tools let an HIM department move from anecdote to evidence-based process improvement.

Finally, distinguish the closely related terms the exam likes to blur: utilization management/review evaluates the medical necessity and appropriateness of care and length of stay; risk management identifies and reduces exposure to loss and liability; and performance improvement raises process quality. All three rely on accurate HIM data, but each answers a different question — necessity, liability, and quality, respectively.