Data Analysis Methods
The data analysis methods used for the research was qualitative derived from the various coded research questions integrated into the interview research questions. Initially, before the beginning of the actual process of the analysis, data were categorized either as primary or secondary depending on how it was acquired and its purpose. However, most the data gathered in this research was primary, meaning it was acquired intentionally. Consequently, its analysis was based on an empirical investigation that led to theoretical conclusions to determine whether or not H.I.E. (Health Information Exchange) contributed to the reduction of medical errors in Saudi Arabia.
The table below shows the various forms of the data classifications used in the research in both the primary and secondary forms.
Data Category Data Type Example
Primary Qualitative Online Questionnaires Response
Primary Qualitative Online Interviews
Secondary Qualitative Research Papers
The analysis of this quantitative data began with inputting of the raw data into groups and then examined to reveal the differences and similarities that each set of data shown. In this case, the amount of information shared about a particular disease was compared with the trends of recovery within the same period when the data sharing in the HIE began. Also, each of the health officers levels of accuracy in correlation with their performance of each disease was tested and determined if there was an improvement or decline in their performance relating to the specific condition. The performance of recovery success before and after the sharing of information was compared to determine the patterns of change. In most cases where there was a considerable traffic of information transmission about a particular disease, tremendous improvements were revealed. Upon completion, the correlation of the peer-reviewed research findings that either supported or contradicted the conclusions was empirically categorized and tabulated to compare with the data themes.
Interview Coding Styles
The research used deductive and inductive coding styles in formulating the online surveys based on the issues that the research questions aimed at addressing. Most of the pre-existing research materials showed a deficit in the various ways that the HIE system significantly decreased the rate of medical errors among the health workers in Saudi Arabia. This technique used the quantitative approach by critically investigating the record of shared information about specific diseases and the ways that those doctors who received the info significantly improved their patient recovery rates. The inductive coding style was used categorize the interview questions for the online surveys among randomly selected clinical officers from the database of users. They were signed a digital declaration form affirming their voluntary participation in the research and guaranteed accuracy in their responses. The main code frames were challenges of the system and reduction rates observed in medical errors in the treatment of patients with specific diseases in Saudi Arabia. The grounded data analysis was adopted and used in this methodology. Each transcript was carefully read and contextualized in the two code frames, and any new codes that emerged were created and re-examined and integrated with the new structure. The themes followed three categories of initial challenges, conflicts arising and the ways the HIE system challenges were resolved. Sections of interview material enabled a theme-focused analysis, formulation of the connections between themes of all the code frames and determination of the order of relevance of the issues to the HIE system users. The results were then compared to secondary data sets of the health patterns of medical errors using Hyper RESEARCH computer software to measure the level of accuracy between the two data sources.
Advantages and Limitation of the Coding Styles
The advantage of these coding styles is that they reveal the specific types of diseases whose margin of error were reduced by the systems. The limitation of this coding techniques was that it did not accurately capture out-patient treated-diseases but only revealed data on the in-patient diseases where the doctor spent more time with the patient.
Code Categories Table
Final Coding Framework Initial Open Code categories
Medical Information shared Shared disease treatment info
Doctor Patient-Recovery Patient recovery rates
Reference to HIE records
Efficiency Levels of HIE system Use of HIE info during treatment
User Frequency of use of HIE info
Medical Error Reduction rates Rates of preventable patient deaths
Patient response to treatment
Description of Grounded theory methodology
The research began with a qualitative data analysis on the available material on the HIE system arising from the identified research gap on the rates of medical error and the ways that the system impacted on the topic in Saudi Arabia. Initial research indicated the existence of limited scholarly literature of the impact of the Health Information Exchange systems in Saudi Arabia as is the case in most other countries. A pilot study involving an interview of randomly sampled medical staff working in the Saudi Healthcare system showed mixed reactions on the topic. However, the majority of participants shows a positive impact with the increased use of HIE among the doctors. The observations of the initial research correlate with the findings in other countries such the USA and New Zealand. Research indicates that information exchange among health care officials leads to advanced knowledge on treatment thereby critically minimizing the probability of the occurrence of error.
Theory of Research
Therefore, based on the results of the pilot study, doctors access more information on accurate ways to treat diseases hence the scales of error reduced. Ease of access to medical information of a patient ensures that the patient's information is captured and integrated into all engagements about their health. As the figure below illustrates, the theory follows a set of various steps that begins with the identification of the research topic.
The second step is the coding process which seeks to determine the underlying theme in the collected data. Consequently, the short phrases that exist in the research reveal the crucial facts of relevant to the topic as shown by the pilot study. The central phenomena explained in this study is mainly in the ways that highly efficient HIE systems in Saudi Arabia are significantly helping the medical practitioners diagnose and treat patients.
This is a result of several causal factors introduced by the system. 83 % of the participants attest to reduced medical errors because they have quick to a patients history. The better informed a doctor is about a patients medical history, the higher the levels of definitive treatment. The fact that they can comparatively evaluate a broad base of patients also help them to review the best strategies to treat even the unique conditions. The next step is the memoing and theorizing of the conclusions that can be derived from the coded information. In this case there several contextual theories that directly impact the strategic phenomena arising from the data analyzed.
Specific Strategies resulting from phenomenon
First, as a result of reduced medical errors, the health care department can institute policy frameworks that significantly address the needs of the patients. Also, the financial planning of health care for patients is more comfortable because they can review their health status and choose the most appropriate packages. However, there is the need for increased political goodwill and social reform policies to minimize the cost of this form technologically advanced health information exchange. These interventions would ensure the holistic integration of the system into the whole Saudi Arabia medical system. The connection of the themes is that higher information sharing increases patient recovery rate. Similarly, efficient HIE systems reduce the medical error rates. The final step, sorting indicated there emerging theory adds no significant information on the existing theory hypothesized by this paper. As such, the anticipated results of the research is highly expected to support the argument of this study that HIE systems are leading to better healthcare with reduced error.
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