Facilities Staffing Requirements for the Veterans Health Administration–Resource Planning and Methodology for the Future (2020) / Chapter Skim
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3 Models Applied to Staffing
3 Models Applied to Staffing
Pages 30-47
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From page 30... ...
This chapter reviews the basic characteristics of models in general and staffing models in particular. This chapter also provides a general checklist of features to evaluate when choosing among staffing models.
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From page 31... ...
The factors that determine risk are based on the causal chain between the input variables and the outcome variables -- in this case, patient outcomes. Linking a variable of interest (e.g., maintenance staffing levels)
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Furthermore, summative evaluation is sometimes seen as an effort to characterize the effects of organizational practices on distal or ultimate organizational goals. When the causal chain linking model inputs (e.g., staffing levels)
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Validity Clearly the most critical model characteristic is its accuracy. In this case, accuracy means, Does the model correctly characterize the levels and characteristics of the input variables needed to produce the desired organizational TABLE 3.1 A Checklist for Evaluating Staffing Models 1.
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From page 34... ...
facility conditions, while referencing Joint Commission goals such as "managing utility systems to ensure operational reliability" and "minimizing fire hazards," listed outcomes such as stained ceiling tiles and "scrapes not patched/painted" in its list of top five most often identified "condition deficiencies." Second, measurement of these variables, which would be critical to an "ultimate criterion model," is a nontrivial task. A well-known finding in the industrial-organizational psychology literature is that performance measures are often highly affected by "criterion contamination" and "criterion deficiency." These issues in measurement are discussed in more detail below, in the section "Staffing Model Outputs." Third, many of the variables that directly affect patient outcomes are typically only distantly affected by maintenance staffing.
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From page 35... ...
A critical aspect of adaptability is the ability of model inputs to incorporate or act as proxies for unanticipated input variables. In the health-care domain, changes in patient demographics, types of services provided, and advances of changes in medical technology may potentially affect model outcomes (in this case, predicted staffing levels)
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From page 36... ...
"White box" models are desirable for transparency. Historically, most staffing models probably qualify as white box models.
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That variability in building age might be a critical input variable, hidden by using average age. Very high correlations between measured variables such as square footage and current staffing levels may appear to imply that the input variables are adequately reliable and valid.
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MODELS APPLIED TO STAFFING Context Staffing models typically operate in the context of a staffing strategy (Bechet, 2008)
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From page 39... ...
, a primary focus of any evaluation of proposed staffing models is the evaluation of the input data. This data must simultaneously be of sufficient quality to produce reliable, accurate outputs and have a practical cost/benefit ratio (in time, money, and less tangible factors such as degree of disruption to operations during data collection)
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From page 40... ...
Direct time studies are traditionally known as time and motion studies, although here the focus is on the time study processes used to determine task duration. Time studies are typically conducted using some form of observation of the task performance (direct observation by humans, cameras, or other mechanical means of recording task times)
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From page 41... ...
noted that the most common input variables in staffing models for clinical engineering were number of beds, number of pieces of equipment, and the total acquisition cost. Their analysis looked at total number of devices, total technology management hours, and hospital complexity and found that these factors predicted current FTEs with a considerable accuracy.
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From page 42... ...
The common practice of aggregating across different organizations to produce the benchmarks may exacerbate this problem. The point here is that evaluations of staffing models should include not only a close examination of the costs of obtaining input data but also a close examination of potential contaminants and deficiencies in the metrics used as inputs.
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From page 43... ...
There are several studies of relationship A, including within the Veterans Health Administration (VHA) itself (see studies of VHA administrative staffing levels)
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VHA in its staffing model efforts does indeed have large amounts of disparate data, collected for other purposes and stored in different databases. This combination of big data and advanced ML techniques to analyze such data holds great promise.
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From page 45... ...
Examples of unstructured data include photos and videos Examples of semi-structured data include emails or text for which it is relatively easy to analyze the meaning of the content. However, it is important to note that all of these data approaches only work with existing data, which include the actual or current staffing levels.
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From page 46... ...
It can produce particularly valid the resulting data (fluctuations during the group after between the employees to other meaningful estimates method provided with staffing specific time periods each round. The dependent variable x variables, such of the probability of the times found are availability -- for such as summer or experts can adjust to be predicted as service delivery, demand exceeding representative both example, total holiday)
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From page 47... ...
Cost and Data Relatively Requires Data requirements Cost associated Time-consuming Requires one of the Requires several Required inexpensive, and considerable are the same as for with obtaining the method and, hence, techniques listed years of data with has small data historical data, econometric models. necessary data could expensive.
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