AHIC Review Course: Clinical Data and Workflow Processes – Workflow Analysis and Re-engineering

A set of practice flashcards covering the key concepts from the lecture on clinical data and workflow redesign, including goals, processes, metrics, data quality, and algorithms.

What is the primary goal of clinical workflow redesign?

To maximize efficiency, enhance healthcare quality and safety, remove workflow chaos, improve care coordination, and maximize value from EHR use.

What are the main steps in the clinical workflow redesign process?

Create a process map identifying the process and stakeholders; evaluate the existing process; consider improvements with a clearly defined goal or outcome.

What is the purpose of a process map in clinical workflow redesign?

To identify the process, stakeholders, and guide evaluation and improvements toward a defined outcome.

What issues can clinical workflow analysis identify?

Redundant tasks, bottlenecks, and lack of efficiency or conformity with best practice.

What tools can assist with clinical workflow analysis?

Workflow modeling tools and process mining tools.

In workflow analysis, who is the 'customer' and why must they be understood?

The recipient of the workflow's outputs; understanding the customer clarifies requirements and success criteria.

What are the four evaluation metrics in workflow redesign and why are they called the 'devil’s quadrangle'?

Time, cost, quality (external and internal), and flexibility; improving one metric may worsen the others.

What is lead time in workflow metrics?

The time it takes to produce one product or service.

What is throughput time in workflow metrics?

The elapsed time between the completion of two activities in a process.

How is external quality defined in this context?

The customer’s perspective, including satisfaction with the workflow’s outputs and processes.

How is internal quality defined in this context?

The worker’s perspective; assessment of job characteristics that are satisfying and motivating.

What role do algorithms play in health informatics and EHR redesign?

They guide decision-making and patient care, support education, and must be updated as new evidence changes; input data quality is critical.

What data issues can affect CDS and machine learning in healthcare?

Data inaccuracies, missing data, lack of standardization, and data not designed for downstream use, requiring data curation and transformation.

Why is data quality crucial for healthcare decision-making and CDS?

Because algorithm outputs depend on high-quality input data; biased or incomplete data can lead to unsafe or ineffective decisions (e.g., bias in claims data).