Part 3

profileJbenson

 

  • 2 years ago
  • 2
files (2)

WorkflowRedesignModelAnalysis.docx

1

Part 2 Model Analysis

Your Name

Institution

Name of Course

Your Professor

Due Date

Part 2 Model Analysis

Summary of the Gap Analysis Results

Gap analysis, also referred to as the need analysis is is needed in EHR today’s comparison to the wanted position in the future as it is needed elsewhere (Conti, 2022). This study, therefore, engages previously sought analysis to establish the results obtained, how they relate to set objectives, and some identified loopholes within the system. The following are some of the results as identified by the analysis. First, the analysis was able to establish the existing gaps between patient registration information and the existing health records information. Ogilvie (2020) shows that the identification of inconsistencies between the patient-recorded information and the already existing health records is one of the steps to reducing the amount of time required to accomplish the refill process.

The process was also able to identify a few more loopholes within the system, which included the following. First, there were delays encountered in the retrieval of patient data in custody with facility clerks due to the inconvenience of the manual filling system. To embrace a smoothly running process, Ogilvie (2020) advocates for the endorsement of an automatic filing system. Next, in efforts to explain the variance in data records between patient-recorded information and already existing file records, the analysis found that inefficiencies existed between the communication models embraced by the medical staff in obtaining the data from the serviced patients, featuring as clients in this case.

Another result of the analysis was the identification of an existing skill gap, which was based on the interviews that were carried out. The interviews revealed that individual members of the staff found it beneficial to have tailored bits of training in particular areas where the workflow as a whole needed to be improved. This can be used to ensure consistent improvement in the workflow. As for the process of refilling medications, the investigation revealed that there is a requirement for the implementation of process automation in order to influence the drug refilling procedure. Given the findings of this model, it is imperative that a greater amount of focus be placed on the implementation of an automated medicine refill system.

How the Results Address Goals Set

From the set objectives, the following is an analysis of how obtained results align to address set goals. First, in the goal set to identify significant gaps, the results obtained can be seen to align with this objective. The results depict that manual procedures used to record patient data for the filling process are a significant contributor to inefficiencies and variances with already existing patient records. In response to this, a set objective to achieve seamless processes aligns with the model results that recognize the need for process automation to avoid discrepancies from the manual system (Zheng et al., 2020). Next, the skill gap aligns with the model objective to address the gaps that work towards enhancing the staff’s capacity to tackle automated processes.

Gaps/Issues in Current Workflow

The identified model analysis effectively demonstrates the major concern in the current workflow as the complexities in the integration of the manual filing system with the automated EHR system (McGonigle & Mastrian, 2022). Before the process reaches the medical assistant, clerks have an earlier interaction with the patient’s data, which they convey to the automated system. Clerks, therefore, appear as intermediaries between the manual filing and the automated system. In the interview questions, however, clerks recommended in-person training with the automated system. Questions also arise about the accuracy of clerks in keying in patient-retrieved data.

Secondly, the variance between retrieved existing client information to obtained data from the communication inefficiencies that arise between clerks and the patient in the data recording phase is attributed. Limited automation in the conclusion part of the refill process also accounts for the inefficient procedure of medical refills. This time-consuming process is evident when the pharmacist has to make manual/ verbal communications to the client/patient explaining medication prescriptions or the reason for failure. Manual communications increase the likelihood of error by lacking real-time updates (Zheng et al., 2020). Thus, it should be eliminated.

Issues Relation to EHRs and any Meaningful Objectives

The major concern of delays attributed to the manual integration with the automated system has a direct relation with the EHR, where clerks interact as intermediaries between both systems. In the meaningful use objective, a conflicting concern arises where a seamless transition is expected, but instead, the system amounts to time wastage. The gap in communication inefficiencies, on the other hand, has a direct relation to the EHR through a denied real-time update of the system. Towards the meaningful objective, McGonigle and Mastrian (2022) show that inefficient communication between pharmacists and patients in communicating refill communications poses a significant safety threat to the patients.

Refined Model

To summarize, the final model, which is depicted in Figure 1, tries to get rid of redundant tasks inside the system to make it more efficient by integrating some manual functions into the electronic health record system. The workflow gap, on the other hand, continues to exist, which involves the clerk still manually involved in the process of processing client requests and acquiring patient data for the reimbursement appeal. However, the system is improved and more efficient once the duties have been transferred to the MA, all of the procedures that are involved are then automated. The system will automatically create relevant answers in the form of text messages, explaining the reason for failure or prescriptions if a refill is permitted, regardless of whether the request is approved or whether it is denied.

Figure 1: Refined model

References.

Conti, S. (2022). Filling the Gap Between Potential and Actual Usefulness of Electronic Health Record (EHR) Data as Patient-Level Evidence.  Medical Decision Making, 42, 973 - 974. https://doi.org/10.1177/0272989X221129229

McGonigle, D., & Mastrian, K. G. (2022). Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett Learning.

Ogilvie, J. (2020, September 2). Patient-centered, integrated healthcare quality measures could improve health literacy, language access, and cultural competence. National Academy of Medicine. https://nam.edu/patient-centered-integrated-health-care-quality-measures-could-improve-health-literacy-language-access-and-cultural-competence/

Zheng, K., Ratwani, R. M., & Adler-Milstein, J. (2020). Studying Workflow and Workarounds in Electronic Health Record-Supported Work to Improve Health System Performance. Annals of Internal Medicine, 172(11 Suppl), S116–S122. https://doi.org/10.7326/M19-0871

image1.png