Non-Nursing and Non-Medical Staff Roles in Hospitals

Section A: Purpose 

The purpose of the study is to establish if non-nursing and none medical staff in healthcare institutions can be entrusted with clinical obligations with the assistance of technology. The area selected for this issue was an assessment of patients to avoid under 30-day readmissions which are inordinately expensive. The hypothesis creating the research issue was that mobile technology can be used to predict readmission risk by non-medical staff (Ostrovsky et al, 2016) . This leads to the research question on whether a member of nonmedical staff with proper mobile technology can predict 30-day readmission risk as accurately as medical staff members. 

Section II 

A: Methodology 

This was observational , not an experimental research as it involved actual patients in a clinical setting. The research was quantitative in nature, involving a total of 1,064 patients being observed, all of them being generally aged patients thus having a high readmission risk. The focus for the research was those patients who were attended by nursing staff and those who were attended by non-medical staff (Ostrovsky et al, 2016) . Mobile phones were used so that the nonmedical staff could operate in consultation with nursing staff who were handling patients. Proprietary software was then used to predict the susceptibility of readmission for patients handled by nursing staff and those handled by nonmedical staff using technology. 

B: Methods Used to Collect Data 

Data was collected from the ESMV administrative databases and the Care at Hand system. This was an effective method since it provided the data necessary for carrying out the research. ESMV provided the professional affiliation of the staff member handling the patient while the care at hand system provided data for use to test propensity for readmission (Ostrovsky et al, 2016) . 

C: Nature of Data Collected 

The data collected was quantitative in nature. It entailed actual figures of patients as divided according to processes and outcomes. For example, there is the total number of patients observed, the number of patients handled by nurses and number of patients handled by nonmedical staff. More importantly for the research, there is the number of patients found susceptible for a 30, 60, 90, and 120-day readmission risk (Ostrovsky et al, 2016) . 

D. Potential Weaknesses to the Data 

For a start, most of the data processing is done using proprietary technology. The accuracy of the data is limited to the accuracy of the system. Secondly, the research is based on a technology that is still being developed, thus the accuracy of the data is limited to the efficacy of the new technology. Finally, the research has a limited scope since secondary factors bearing upon readmission are not factored thus reducing its validity and reliability. 

E: Data Analysis Methods 

There are two main methods of data analysis being Logistic regression and ANOVA. The first entailed the evaluation of the illness trends, treatment, and reactions of the patients to help evaluate their susceptibility for readmission. ANOVA, on the other hand, was used to come up with the subjects’ analysis of variance (Ostrovsky et al, 2016) . These two processes enabled an understanding on the effect of the officer handling the patient towards readmission. 

F: Potential weaknesses of the data analysis methods 

Traditional data analysis was based on an understanding of the situation and the data itself. It could also be gauged according to the expertise of the experts evaluating it and the formula that arrived at it. However, all the data evaluation in this study was done using proprietary software whose particulars cannot be exposed. This is a manifest weakness. Further, there are too many variables to enable extrapolation. 

E: Patient Demographic 

The only patient demographic factored is age, yet without specification save for stating that the patients were Medicare and Medicaid beneficiaries between 66 and 80 years of age. 

Section III 

A: Key Findings 

There were many findings within the study, many of which did not feature in the discussion and conclusion of the research because they were not found to be pertinent. The pertinent findings were those that could be used in answering the research question on if technology can make a difference in helping nonmedical staff handle patients resulting in a reduction of 30-day readmissions (Ostrovsky et al, 2016) . An example of data that was not factored in the discussion was that relating to patients who were handled by doctors and nurses. 

Therefore, the pertinent findings were the readmission rates when nonmedical staff did and did not use technology to assist them as they handled patients. 30-day admission rates across the groups were 32%, 20%, 29% and 14% respectively for non- medical workers based input and 33 %, 22%, 20% and 14% for the nurses based ranking. ANOVA result as well as between-group t tests yielding p-value < 0.01. In nurse-based ranking, the groups high (33%) and moderate risk (22%) were statistically insignificant with p-value =0.06 (Ostrovsky et al, 2016) . 

B. Differences in the results 

The first pertinent set of results shows the readmission rates of patients who have been handled by nonmedical staff whose capacity and expertise on the handling of patients is limited. However, their capacity is enhanced by the use of technology. The same is compare by readmission rates of patients handled by nurses who have a higher expertise and skills (Ostrovsky et al, 2016) . The pertinence difference within the results is not the discrepancy but rather the congruence. The results show that there is a very slight difference between readmission rates when nonmedical staff members are involved and when nurses are involved. This means that technology exponentially advances the capacity and capability of the nonmedical staff to make it almost congruent to that of qualified nurses. Considering that the technology is still at development stage, the minuteness of the difference indicates the great capacity of the technology (Ostrovsky et al, 2016) . 

Section IV 

A: The limitations presented by the study population and sample size used 

The sample size was small, considering the fact that not all of them could have been used for the research study. Majority of the patients in the study were not handled by nonmedical staff or nurses that could not be used in the comparison (Ostrovsky et al, 2016) . This resulted in an exponentially lower actual sample for the research and, therefore, a lower level of reliability and validity. On the other hand, the study population was not divided demographically in any manner. Demographics such as exact age and sex are pertinent when matters or health are concerned so does race. Non-consideration of this factors acted as a limitation to the entirety of the study. 

B: Pros and Cos of the Data Analysis Used 

Technology as a means of data analysis is a new concept but one that has taken over the world of research in both quantitative and qualitative research. Mass amounts of data can be derived from a research process making its analysis take an exponentially high amount of time and money (Ostrovsky et al, 2016) . In some cases, studies would be limited purely by an inability to handle the magnitude of data. Using software technology for data analysis solved all this making of a major advantage. No matter how vast data is, once parameters are properly set and fed into the system, results can be arrived at in a comprehensible state within a very short time. The main disadvantage of technology in data analysis is that the analysis has to be accepted by the auditors of the study at face value since it cannot be verified without using the technology. This creates a gap on the synthesis of the study once it is published (Ostrovsky et al, 2016) . 

C: Limitations of the Study Design 

The instant study is an observatory , not experimental meaning there are many aspects of the same that are beyond the control of the research team. Replication of the research would, therefore, be difficult seeing that there are some aspects of this that are beyond any form of logical control (Ostrovsky et al, 2016) . This is the most significant design limitation. 

Section V 

A: Results Comparison 

The main research shows that the capacity of members of staff in a clinical setting can be improved by technology. In the main research, the capacity of nonmedical staff is lifted to almost that of medical staff through the use of technology (Ostrovsky et al, 2016) . This has congruency with the secondary research where technology also enhances the capacity of staff members in a clinical setting. The secondary research relates to e-prescribing a system that incorporates technology to the process of drug prescription (Kaushal et al, 2010) . According to the conclusion of the research, e-prescribing exponentially reduces the rate of error in prescriptions. This aligns with the research in the primary article that shows technology playing a positive role in assisting clinical staff. 

B: Evaluation of Study’s contribution 

Technology has become a force to reckon with in all industries including the medical field. In many cases, technology has been used as a tool in the hands and control of clinical staff. In this scenario, technology is being used as a means of advancing the competency and effectiveness of clinical staff. The study introduces a concept of partnership between technology and staff which is a whole new area of study. Clinical practice is based on strict qualifications and competencies. That technology can raise the level of competence of a nonmedical staff to perform clinical duties makes for a great area for research and study. 

C: Further Research 

The first area for further research is an elimination of the limitations or the mitigation of their adverse impact. With this being technology, it would be better for an experimental study to be undertaken where all dynamics could be under control. Secondly, similar research can be undertaken with a wider population sample that also includes demographic aspects of the patients. Finally, as the technology in question is still under development, further research would indicate if the development creates any adverse contingencies. 

References 

Kaushal, R., Kern, L. M., Barrón, Y., Quaresimo, J., & Abramson, E. L. (2010). Electronic prescribing improves medication safety in community-based office practices.  Journal of General Internal Medicine ,  25 (6), 530-536 

Ostrovsky, A., O'Connor, L., Marshall, O., Angelo, A., Barrett, K., Majeski, E., ... & Levy, J. (2016). Predicting 30-to 120-day readmission risk among Medicare fee-for-service patients using nonmedical workers and mobile technology.  Perspectives in Health Information Management ,  13 (winter), 1-20 

Appendix One