There are various adverse experiences that patients encounter when they seek medical services in a healthcare centre. It should be noted that the said complications are different from the main reason why the patient visited the hospital. Some of these complications originate from the status of the health facility itself where the patient is admitted (Alyousef et al., 2017). The Patient Safety Indicators (PSIs) are a set of measures taken to show some of these adverse experiences the patients have due to exposure to the systems within the healthcare centre. These adverse experiences can be prevented by merely improving the conditions that promote them or changing the system in the health facility. The definition of Patient safety indicators can be grouped into two distinct categories namely provider level indicators and area level indicators. Provider level indicators enable measurement of the avoidable adverse experiences by the patients who received their initial medical care and experienced the complications in the same hospital. Those indicators involve the only case where a secondary diagnosis code causes another avoidable difficulty (Carls, Henke, Karaca, Marder, & Wong 2015). Area level indicators showcases of such experiences associated with a given area like a country or a town. In this type of patient safety indicators, even cases of a patient suffering some complications after being readmitted in another hospital within a given area are included.
It should be noted that previously, the measures that were used to evaluate such incidences were confined to capture cases in the inpatient’s settings only. However, the current PSIs possess a step in development by including all other areas in the hospital and even in a given geographical area according to Clark, Weinreb, Flahive, and Seifer (2018). This paper focuses on globally accepted PSI developed through the dynamic four-step process of literature review, evaluation by clinical practitioners, codes reviewed by industry experts, and finally subjected to empirical analyses to ensure applicability. This study focuses on how the use of Veteran Affairs (VA) discharge data improves the patients’ safety as compared to cases where VA discharge data is not applied (Cohen, Cohen, Stagnitti, & Lefkowitz 2016). The study concentrates on the use of version 2.1 of the software.
The development and application of VA discharge data have enabled the managing of potential threats to patients’ safety in hospitals and the user as a benchmark for hospital performance. The use of this technique has however raised concerns that it increased mortality, prolonged stay in the hospital and increased hospital charges. The importance and the roles played by the software have however surpassed the concerns raised.
The primary objective of this paper is to show the development and applicability of the Veteran Affairs (VA) discharge data and compare the performance of hospitals that use the software and those that do not use it (Dorflinger et al., 2014). The software was developed and tested using computerised hospital discharge data, and therefore the definitions used here are based on variables from standard hospital discharge abstracts. The abstracts used are generated from the clinical and nonclinical data elements that are regarded as institutional claim standard. It is worth noting that VA databases are developed using unique formatting system that exhibits both acute and no acute care. This is entirely different from other widely used hospital administrative databases that only contain standard discharge abstracts (Helm et al., 2016). The paper intends to generate a valid indicator rate utilising the software and draw a comparison between the frequencies used in VA and those hospitals that do not apply VA discharge database. Another target of this study is to present the current knowledge to future researchers and practitioners who may want to use the information in future.
Some simple information worth knowing regarding this software is that Veteran Affairs has an administrative database that consists of information on diagnosis, demography, and how the resources were utilised on all the veterans who visited healthcare centres in VA. The primary variable being analysed here is the hospitalisation, which is linked to the veterans through datasets and fiscal years and accumulated at the individual veteran level since each veteran has a unique identifier (Gellad et al., 2018). The system uses the Patient Treatment File that contains both acute and nonacute hospitalisation data on the patients discharged from facilities that use VA software. Several hospitals across the United States currently supply this database. This file is further divided into smaller subdivisions to give specific information on the various sections of veterans’ treatment process. The subdivisions are principal, bed section, procedures, and surgery. These subdivisions contain different pieces of information that help in processing the whole system.
The central subsection has information based on demographic data such as sex and age of the patient, diagnostic and summary of the information in the related stages of medication (Kohli & Tan 2016). The Bedsection file includes information on which treatment the patient received and for how long. Based on this kind of information, a veteran can have more than one section record in one hospitalisation. The Procedure subfile contains data relating to all the procedures that the patient underwent during his or her stay in the facility. Such information includes date, time, and place where the procedures were done (Mages & Kubic 2016). Finally, the Surgery subfile has data on every surgical procedure the patient went through.
The VA software uses algorithms that link various pieces of data from different subsections of the patient’s treatment process to come up with the PSI event rates. For instance, the software brings together codes on procedure and diagnosis with other pieces of information different standard data sets (Peterson et al., 2018). This software requires some raw data to generate the intended rates, such data may include but not limited to hospital identification number, age, date, admission type length of stay, and secondary diagnosis codes. It should be noted that the software uses data presented in a statistical format such as Statistical Analytical System or Statistical Package for the Social Science.
Most of the data types like age, sex hospital identifier, race as well as primary and secondary diagnosis used in the software are already in the VA files and therefore required no modification. However, there are some types of data that such as principle procedure, admission type and days from admission to procedure need to be modified in the same format as the other applicable data (Wilner & Ghassan, 2017). The illustrations below show how the modification of each of the above data can be achieved. Additionally, some pieces of data were initially missing in the software and had to be incorporated just like the existing data elements; like the admission source.
The four files used in the VA data system have much more information about a patient as compared to the files that are widely used Healthcare Cost and Utilization Project (HCUP) data. Some of the areas that make VA software more superior include its ability to link many pieces of information about a patient, like the various hospitalisations the patient had. Another area making VA more suitable is the fact that it contains data from bed section which give information on discharge time, procedures types and times, admission dates and the primary and secondary diagnosis codes. As illustrated by Sutton, Eborall, and Martin (2015), VA software accommodates both acute and noacute care while the Healthcare Cost and Utilization Project contains only acute care.
The main aim of this study was to formulate test the methods of putting PSIs into use through VA discharge data and at the same time acknowledging the differences and similarities between rates of Patient Safety Indicators with VA and those with no VA discharge data. The various modifications on VA inpatient administrative database were meant to reduce the differences between VA database and the other software that perform the same function but do not use Veteran Affairs databases. It should be noted that the modifications solved the first challenge that some of the basic requirements were missing or given a different definition from the ones that could be read by the software (Wilner, & Ghassan 2017). It was also clear that the various modifications had different impacts on the database characteristics and the PSI rates. For example, alterations like designating principle procedure and admission impacted more than other changes. It is worth noting that the various rules used in designing principle procedure and all other secondary procedures ended up affecting both the numerators and denominators of the final PSI rates. At the same time, the algorithms used affected both elective admission rates as well as the other PSI rates.
We realised that the leading cause of the difference between the VA databases and non-VA databases is the fact that the VA database has nonacute care while non-VA databases have only acute care. For this reason, the exclusion of the nonacute care was necessary to create a level ground for comparing the two sets of databases then reaggregate the remaining acute parts of the mixed hospitalisations (Xie, Li, Swartz, & DePriest 2014). The new features on the reaggregated admissions resulting from the alterations made on file structures further required for more changes in the affected data elements. Such changes included principle procedure, length of stay and principal diagnosis. This moderation between VA and other sets of databases resulted in a levelled comparison ground even though it reduced the amount of information in the VA database and diluted its actual image.
The fact that there were evident changes in the PSI rates between the VA database with acute care and that with both acute and nonacute care dhow that the rates significantly depend on the data. Another conclusion drawn from this scenario was the ability of the VA database to identify potential patient safety with or without acute care setting (Reimer, Schiltz, & Madigan 2016). The variations in the PSI rates between the original VA database and the acute only VA database may be attributed to the changes in principle procedures, principal diagnosis, and some procedures codes used. Any change in any of the elements of hospitalisation record leads to either inclusion or exclusion in the PSI numerator or denominators.
From the above illustrations, the application of Veteran Affairs software in measuring patient safety in a healthcare centre has more advantages that the application of other methods according to O’Brien et al. (2015). However, many critiques have argued that the method carries risks of capturing wrong data from false events. The modifications applied to the VA database may not work in other systems that try to adjust to implement the PSI software. Our results suggest that other healthcare systems may face similar needs to make modifications to their data to apply the PSI software (Qiu & Wang 2018). Even though previous man studies indicate that there is the possibility of modifying the element data to achieve compatibility in other systems in the healthcare industry, the resulting changes may affect the comparison between the final PSI event rates. Most of the studies considered here did not establish the risk-adjustment factors to derive how flexible the system works. This in return paves the way for further studies in future in that area of research.
According to Hill, Lind, and Daraiseh (2015), the use of VA software as a patient’s safety indicator in the hospital setup implies some level of improvement in performance of the healthcare facility. This development fits into the PDSA cycle as illustrated bellow.
At this stage, thorough study is done to learn the VA discharge database software in details, its applicability, and the impacts the software would have on the regular operations of the hospital (Kohli & Tan 2016). The decision to use the software majorly depends on the goal being achieved, improving the patient safety in a hospital.
This stage involves applying the actual program or VA discharge database software in this case (Wilner & Ghassan 2017). The software is installed and allowed to run within the already existing structure in the hospital.
The first stage of the actual application should be studied over some time to analyse the real impacts the software has in the hospital (Qiu & Wang 2018). From the illustrations above, the VA software was confirmed to be working well with the other systems in the hospital
With no much alterations needed based on the analysis stage, the Veteran Affairs discharge database software can be fully implemented in the healthcare centre to improve the safety patients and other users of the facility (Kohli & Tan 2016).
Conclusion
From the above study, we realise that patients are at risk of experiencing adverse events when exposed to some systems in healthcare centres. Such events can either be either provider level indicators or area level indicators. One of the effective ways of measuring the Patient Safety Indicators is the use of Veteran Affairs software that uses data from Patient Treatment Files which categorises data into for subheadings namely main, bed section, procedures, and surgery. The VA database software is considered advantageous in that it accommodates both acute and nonacute care hospitalisations. However, the application raises some concerns in that we have demonstrated the sensitivity of PSI rates to differences in data file structure and definitions and sources of data elements (Muhammad Zia Hydari, Telang, & Marella 2015). The consequences of this sensitivity are amplified by the fact that PSI rates are inherently low: most PSI rates are in the range of one to five per thousand hospitalisations. Therefore, differences in data structures and algorithms that add or subtract just one or a few cases from the numerator of a PSI for a given population and period could make a meaningful difference in the overall PSI rate.
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