By Joe Locke, AAS; Kelly Wright, MS
The benefits of using data to make decisions and drive change outweigh the costs of collection or analysis. Data is impartial, objective and emotionless, and it transforms into information only with context.
Care must be taken to ensure that data is not sought to support or disprove a hypothesis; instead, the data itself leads to conclusions that can be recreated empirically to validate that hypothesis. Empirical research – based on observations and experiences, rather than theory or belief – takes one of two forms: qualitative or quantitative.
Qualitative data
In qualitative research, observations or judgments are made about a thing or action, which are recorded as descriptive words. Examples include condition (e.g., good, fair, poor) or risk severity (e.g., low, moderate, high, critical).
From an analytic perspective, it is difficult to measure or rank qualitative data against other qualitative data because of the subjectivity of the criteria being used – who decides what is good, fair or poor?
Quantitative data
Opposingly, quantitative research is empirical research in which data is in the form of numbers and lends itself well to comparative analysis.
Converting qualitative judgments into quantitative data is a powerful analysis tool, making it possible to generate meaningful, actionable information.
Evaluating employee performance
Without the ability to convert qualitative data to quantitative data, it can be easy for intentional or unconscious bias to be introduced into a qualitative assessment like employee performance. Assessment of employee performance based on metrics is increasingly common as it enables employers to empirically quantify how well or poorly an employee performs with data in the form of numbers. When the employee is an EMS provider and poor performance could potentially affect patient outcomes, it is vital that an organization’s leadership implements impartial assessment and objectivity when assessing work quality.
If strict adherence to the data collection methodology is not maintained, outcomes can be manipulated to favor or disfavor individuals through intent or negligence, potentially perpetuating harmful systemic power structures. Organizations look toward data-driven metrics to evaluate individual and team performance to prevent inklings of nepotism or favoritism and promote transparency and accountability across a group. This provides leadership with concrete data-turned-actionable information to provide critical feedback to individual EMS providers, suggestions for improvement across the department, or opportunities to redefine the assessment criteria if results are inconclusive or irrelevant.
The knowledge that these assessments are being performed department-wide can lessen the sting of constructive criticism at the individual level, offer the EMS provider concrete steps to improve patient outcomes, help the department recognize appropriate EMS billing for services provided, and engender a feeling of responsibility and place within the organization.
About the authors
Joe Locke, AAS, is EMS coordinator, City of Monroe Fire department.
Kelly Wright, MS, is a City of Monroe GIS specialist.