Performance measurement in healthcare is responsible for systems improvement, accountability, expansion, growth, and effective decision-making process. It requires collection, storage and use of data and information on tangible recordkeeping systems. Measuring performance attribute to the identification of standardized data sources viable for conclusive aggregation, understanding, analysis, and dissemination of collected information (Gilbert, 2015). Using the three performance factors in measuring healthcare activities; structure, process, and outcome it is possible to culminate understanding results in any measurable events in a hospital. This paper will discuss the use of the three performance factors; structure, process, and outcome when applied to sources and calculate the results for a hospitable admission process. It will also show the data sources and the reliability of the collected data stipulated by the three performance measurements.
The three classification of measuring performance in healthcare organizations are effective in assessing the quality of services provided for improvement purposes. Structural measures cater for the hospital capacity, processes, and systems able to offer high-quality care. These include the integration of electronic medical records or have elaborate medication order entry systems. The physical structural system also provides the ratio of providers to patients admitted to the facility, and the proportion of board-certified practitioners. On the other hand, the process measure of performance entails the practices of the health providers in providing, maintaining, or improving the health condition of diagnosed patients and healthy persons. Thus, process factors in measuring of performance comprise of the typical reflection of the acceptable recommendations pertaining clinical practices which include the number of people a facility can offer preventive services, and the rate of diagnosis and control. The process measures offer information medical customers on health care services purported to gain the conditions or diseases they suffer to improve the health outcomes. Moreover, outcome measures reflect the impact attained in healthcare provision and intervention measures to mitigate or solve patient's health status. The outcome measure is thus, the result count of successes and failures of medical procedures in hospitals which includes surgery results, treatment complications, or hospital-acquired infections. Outcome measure entails numerous factors to determine the results which are dictated by risk-adjustment techniques used to reduce misleading or inaccurate quality information.
A structure measure applicable to determine the efficiency of hospital facility's administration process requires identification of an internal and external structure like Data Envelopment Analysis (DEA). Adopting DEA structure of performance measurement ensures that patients' information remains confidential and that the preventive techniques applied by practitioners are sensitive and accurate. Using an effective structural measure of performance helps the hospital reduce carelessness and negligence in handling patients' files and confidential records which include improper disposal of medical records after the transition of medical records from manual records to electronic healthcare information system (Hutchinson, 2013). Therefore, adopting and integrating the Data Envelopment Analysis offers a structural performance measure to ensure the electronic transition of high-quality level for effective handling of confidential information of patients (Marsden et al. 2017).
The DEA model forms and effective process performance measure used to determine the efficiency of the hospital administration and healthcare management of the general performances. The system helps the admitting personnel collect accurate information about a particular patient during an appointment by documenting essential background information about a patient that is relevant for identification and other identification processes. Consequently, the process measure needs to be discrete to ensure that during data entry a patient does not get access to information about other patients in the process making electronic information only accessible by the medical staff. The outcome measure requires a valid technique to show the results of the processes carried out in the admission process.
The three factors of measuring performance in healthcare admission depict fundamental characteristics that are essential to distinguish on their application in the hospital's operation process, especially for the admission requirements. The structural measures inform on the individual practitioner's information and services accessibility of the quality of healthcare delivery. These structure measures include practitioner's board certification, accreditation, physical attributed, licensure, and policies and procedures governing a particular facility. The important measure of the processes includes the diagnosis and management focus of a health facility. The measurement is committed to enlightening on the health plan adopted by an individual facility which includes convenience, interpersonal care, and timeliness for effective decision making. The outcome measure entails the orthodox techniques of evaluating the eventualities and unintended outcomes in treatment to ensure that practitioners and patients are aware of the comprehensive effects realizable in any intervention strategy. Therefore, outcome measure determines the functionality limited to the daily routines owing to the patient's satisfaction strategies (Mannion & Braithwaite, 2011).
The quality performance measures are constructed using specified numerators and denominators. The performance measurement requires being constructed using various ways which include proportional percentage, means, ratios, medians, of frequency counts. Each of these approaches plays a functional role in the specific circumstances appropriate for their use in clinical analysis of empirical data. In each of the constructed measurement used to evaluate performance the unique properties applied dictate the properties to be used. When constructing quality measure as percentages or proportions, the denominator representation shows the number of persons admitted for treatment in a facility during a specified period of time. The numerator of the performance measure entails the number of patients at risk or eligible to facility instigated procedure. For instance, the denominator shows the total number of patients who visited a hospital seeking medical help while the numerator presents the number of patients in the denominator who were diagnosed and treated for a particular ailment (Woodrow & Oatley, 2013).
Adopting the percentage/proportion technique in constructing performance measure offers practitioners simpler and easier measure to understand the performance count for the health care providers and express their efficiency to the patients. Consequently, when using the ratio technique to measure performance factors, the denominator is the proxy real population at health risk and lacks a numerator because it is contained within the denominator. The accuracy of the ratio calculation of performance measures cannot actually be enumerated but the use of the census data from the population range in ratio to the number of patients received within a residential or geographical region helps in the construction. Both the mean and the median values are constructed using the directionality of the healthcare provider to distinguish differences. Frequency counts entail numeric tallies without any specifications of the patients at certain risk. The counts are used for adverse outcomes in the surveillance essence rather than comparison purposes (Ashton, 2015).
In the hospital's registration area gathering data from the patients during admission suitable for reporting the performance measure results, various sources can be used to provide possible required data. The use of computer log will help to source data for measuring the downtime hours per a specified period. Other sources of information related to productive hours can be sources from the admission department supervisor, while other essential information about the staff registration requires sourcing information from the human resource department. Patients' data with identification bracelet sources viable data from the nursing staff incidence check. The various destinations to source for patients' data depending on the medical staff responsibilities will provide reliable information to ensure that collected data will give valid and reliable information.
Ashton T (2015). 'Measuring health system performance: a new approach to accountability and quality improvement in New Zealand'. Health Policy, vol 119, no 8, pp 999-1004.
Gilbert, S.M. (2015). Revisiting Structure, Process, AND Outcome. Wiley Online Library, 121(3), 328 - 330.
Mannion R. & Braithwaite, J. (2011). Unintended consequences of performance measurement in healthcare: Twenty Salutary lessons from the NHS, Internal Medicine
Marsden, E., Taylor, A., Wallis, M., Craswell, A., Broadbent, M., Barnett, A. & Glenwright, A. (2017). A structure, process and outcome evaluation of the Geriatric Emergency Department Intervention model of care: a study protocol. BMC Geriatrics, 17, 76. http://doi.org/10.1186/s1277-017-0462-z
Woodrow, P. & Oatley, N. (2013). Practical Approaches to Theories of Change in Conflict, Security & Justice Programmes, Small Arms Survey publication for the DFID Conflict, Crime, and Violence Results Initiative (CCVRI). Available at http://www.smallarmssurvey.org/fileadmin/docs/M-files/CCRVI/CCVRI-theories-of-change-part-1.pdf
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