This paper focuses on establishing the variables that can be measured during the evaluation of the performance of a healthcare’s admission process. It is based on the arguments entailed in the Donabedian model of examining the quality of healthcare, which addresses the structure, process, and outcome performance factors of a given healthcare process. Particularly, on structure, the paper proposes measuring the number of computer system downtime hours registered in the admission department in a month. Regarding process, the study measures the percentage of patients getting to the nursing units with identification bracelets on their wrists. Lastly, the study measures the satisfaction levels of the patients regarding the admission process to evaluate the outcome performance factor. Recommendations on streamlining the healthcare’s structure and outcome performance factors are provided on this particular healthcare process.
Process Improvement
According to Donabedian model of examining the quality of care in health facilities, structure, process, and outcome variables can be used to categorize and measure the quality of care prevailing in a healthcare system (Stelfox & Straus, 2013). The structure is used to illustrate the context in which healthcare is delivered, and is inclusive of the staffing, equipment and the financing of the entire healthcare procedure. Process in the model refers to the transactions held between the patient and the healthcare providers through the healthcare course while outcomes indicate the effects which develop on the patients’ and the population within a healthcare facility after the administration of a healthcare process. Donabedian performance factors are used to determine the efficiency of a healthcare process in addition to providing better approaches of adjusting healthcare delivery processes. This paper considers the improvement of a hospital’s admission process by evaluating the quality of structure, process and outcomes performance factors right from booking the patients in the registration area, crosschecking the admission prerequisites, to patient’s admission in various nursing units.
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Structure
The management should measure the number of hours that the registration department suffers computer system downtimes. This will require the evaluation of the total downtime hours recorded on a monthly basis. Since an absolute number indicates computer systems downtime hours, there will be no need for a numerator or a denominator (Lenz & Reichert, 2007). While several structural aspects are entailed in the admission process, the equipment used for patients’ registration is profoundly optimized through the admission process to ease the cataloging processes. Notably, computer systems are used in crosschecking the identity of the patients, and the viability of the insurance transactions to verify the patient’s sum assured and validity of the policy. Therefore, computer systems downtime hours cripple the productivity of the hospital facility.
Process
To measure the process performance factor, the hospital management should establish a measure of the percentage of patients who arrive in the various nursing units with the identification bracelets on their wrists. Measuring this percentage will entail the numerator and the denominator variables. The numerator variable denotes the number of the patients who reach the nursing units with a bracelet on their wrists while the denominator indicates the total number of the patients who get to the nursing units through the patients’ admission process. The identification bracelet identifies patients who have undergone the registration process effectively and have been examined by the physicians. Evaluating the admission process eases the transition timeframe from the initial admission registration desks to when patients are allocated various nursing units (Stelfox & Straus, 2013). Conversely, patients who get to the nursing units without bracelet identifications on their wrists delay the admission process as they are not hastily identified within the nursing units. Such instances reflect the inefficiencies within the admission procedures that hinder the performance of the patients’ admission.
Outcome
The outcome performance factor of the hospital registration area will be evaluated by measuring the percentage of patients who are satisfied with the facilities admission process. Measuring the patients’ satisfaction levels necessitates the establishment of a satisfaction survey that seeks feedback for the entire hospital admission procedure. Specifically, the measurement will cover the events that take place from the time of a patient’s arrival in the hospital registration area to the time of departure from the admitting area. This measurement will have a numerator that denotes the number of patients satisfied by the health facility’s entire admission process, and the denominator, which will refer to the total number of the patients who respond to the satisfaction survey. Customer satisfaction is crucial in evaluating the performance of any service provider ( Campbell, Roland & Buetow, 2010 ). The feedback received from customer satisfaction surveys provides precise directives for detecting specified procedural problems in healthcare processes. Therefore, determining the outcome of the satisfaction variable will provide insights on the healthcare facility’s operations management.
Data Sources
To fetch reliable data for analysis, appropriate data sources are needed. In measuring the number of computer system downtimes registered in a month in the admissions department, data from the computer downtime log will be utilized. Indeed, extracting data from the computer systems log ensures credibility and precision of the recorded downtimes (Lenz & Reichert, 2007). It is also worth mentioning that the admissions department manager maintains the data. With the recent advancement in information technology, there is a need to save important data within various healthcare facilities. The data used in measuring the percentage of patients who get to the nursing units with identification bracelets on their wrists will be fetched from the records kept by the nursing staff. The data obtained from the nurses’ reports enable the facility to keep track of patients’ incident reports and consequently informing on the most optimal healthcare resolutions. Finally, on the outcome performance factor, measuring the percentage of patients satisfied by the hospital’s admission process will entail the development of a dataset from the responses in the satisfaction survey. The dataset will be created from the surveys returned to the hospital facility. The survey method of consolidating response data is influential in the process because it reflects the immediate feelings of the survey respondent, which reports on the present position of the healthcare’s admission process in terms of quality ( Heslop & Lu, 2014 ).
Conclusively, the healthcare facility will benefit from considering the structure, process and outcome performance factors prevailing in the hospital admission procedure. However, the study recommends improving the admission process through the readjustments on structure and outcomes performance factors. Firstly, the facility should measure the number of computer systems downtime hours registered by the admission personnel in a specified month. This will enable the healthcare facility to determine the adequate power back-up needed to counteract the effects anticipated from the computer systems downtime. Secondly, the hospital facility should consider evaluating the percentage of the patients who report being satisfied with the approaches used in the admission process. This measurement is crucial in assessing outcomes performance factor. Apart from providing the depiction of satisfaction in different admission stages, the survey reports offer a more precise overview of the respondents’ perceptions regarding the admission process.
References
Campbell, S. M., Roland, M. O., & Buetow, S. A. (2010). Defining quality of care. Social science & medicine , 51 (11), 1611-1625.
Heslop, L., & Lu, S. (2014). Nursing ‐ sensitive indicators: a concept analysis. Journal of Advanced Nursing , 70 (11), 2469-2482.
Lenz, R., & Reichert, M. (2007). IT support for healthcare processes–premises, challenges, perspectives. Data & Knowledge Engineering , 61 (1), 39-58.
Stelfox, H. T., & Straus, S. E. (2013). Measuring quality of care: considering measurement frameworks and needs assessment to guide quality indicator development. Journal of clinical epidemiology , 66 (12), 1320-1327.
