Professional Portfolio

Running Head: CURRENT-STATE WORKFLOW 1

Current-State Workflow

Name

Walden University

NURS 6421 Section 01, Supporting Workflow in Healthcare Systems

October 16, 2016

Current-State Workflow

Gap analysis is important because it guides those people involved with planning for in improved performance. It acts as a tool for measuring the level of achievements regarding targets by an organization (Dennis, Wixom, & Roth, 2015). It compares the gap between the actual and potential performance of an organization. In this project, I have been working on gap analysis regarding incomplete medical reconciliation. Data was collected, analyzed, and its significance with the set objectives of the gap analysis identified. The purpose of this paper is to explain the results of the gap analysis concerning the goals, analyze the issues identified about electronic health record systems (EHRs), describe the final version of my Visio model, and then explain the changes I made on the Visio draft based on the critiques by colleagues.

The gap analysis conducted in the organization I work for offered me the opportunity to understand the process of medical reconciliation and its discrepancies in our health care settings. From the data collected, it was clear that the existing hospital EHRs was directly tied to the medical reconciliation gaps which existed. In most cases, the information offered by the EHRs regarding the patient's medications was in many instances not updated. Therefore, the medication lists mostly in the EHRs was not actually what the patient was taking. This was available in approximately 55% of the cases. The medication orders were okay, but the list took from the patient differed greatly. Therefore, there was a gap in the harmonization of the patient's medication list in the EHRs.

The same problem resulted in the transfer of inaccurate medication list across the points of care in the hospital. Normally, medication lists are made regarding those received by the patient. The lists are then transferred to the next provider. 30% of the cases in the hospital did not practice this. The provider to which the patient was transferred was forced to revise and make a verified medical list, and if they were not keen enough, they would use the wrong lists in the EHRS which were not updated in most a times. Consultants and specialists who handle patients are also instrumental in reducing medical errors. They are also the key figures which can be utilized during medical reconciliation. The consultants are the seniors, followed by the direct care providers. In the workflow, each has a role to play. In cases they are not included, medical errors are likely to occur. Accountability is important in the delivery of quality care. Each member who is involved in the health care should be responsible and have clear roles in managing the patient's care. There has to be a team that sends a patient and at the same time receives the patient. All of this teams should focus on patients discharge and view themselves as part of the continuum care which ensures a successful transfer (Bowman, Flood & Arbaje, 2014). The primary objective of this teams is to take responsibility for completing the transfer forms, communicate across all the setting on behalf of the patient and make sure all the patients understands their role in the process of reconciliation.

Referring to the goals stated in the gap analysis, the information in the hospital system is not well harmonized. I aimed to determine the strategies to engulf the present medication reconciliation discrepancies, explore various drug identification modes, and to come up with better modes and lastly, engage different stakeholders in medication reconciliation including the consultant physicians, nurse practitioners, and registered nurses. By this, the existing medical reconciliation discrepancies were found to be the harmonization of the medication lists in the EHRs in addition to patient, and clinician information on the current medications. The findings are consistent with the goal of increasing patient and clinician knowledge and communication about current medication. A strategy needs to be instilled so that the patients and clinicians are aware of the current medication. With this, most of the medication errors will be eliminated. When the patients and their primary caregivers are updated, the right medications will be taken (Campbell et al., 2009). The third goal involves improving decision-making at the point of care. This entails the utilization of stakeholders in the medical reconciliation. In improving this medication reconciliation, the conventional drug which has data elements, personal medicine list, and codes should be incorporated in the EHRs

Findings and EHRs

By using the EHRs to reconcile the medication, it will lead to an improved care in the coordination of medication during the transition between the points of care within different hospitals. The process will improve the health care quality, reduce the errors that result from medication and enhance better outcomes (Agrawal, 2009).

Harmonization of medication lists for patients and clinician majorly relates to EHRs. The use of EHRs provides an opportunity to update medication lists whenever orders are made. I came to learn from the data that most of the staff found the utilization of the EHRs difficult because of the different codes for medications. Therefore, the medication lists need to be harmonized from the system, and then errors will be reduced towards the patient. The EHRs presents the best way to coordinate care during the transitions of patients between sites of care (Wright et al., 2011). By handling the issues related to EHRs harmonization, thereby the goal to improve care coordination during transitions between sites of care. Accurate and timely information that is provided to the patients, family caregivers and the providers are critical to an effective care transition. At any point, it involves different individuals and sometimes many transfers among the various medical settings. If there is poor communication during the development process, then there will be increased rates of hospital medical errors, readmissions and defiantly poor health outcome (Agrawal, 2009). Some care changes like from emergency to medical outpatient departments require a lot of attention because there is a lot of care which is needed by the patients that involve different nurses, social worker, and family caregivers that may result in handoffs creating opportunities for communication inefficiencies. By adopting the use of electronic health records (EHR), the flow of information will be significantly improved between the providers and lead to a better, accurate, complete and accessible clinical information (Unertl et al., 2009).

My final Visio model starts with the input of drug information. It is the first step of admission. Afterward, patient information is entered. These two types of information are entered into the EHRs by a system administrator. The system administrator is responsible for the input of the information regarding drugs and patient. The administrator, in this case, is the caregiver at the admission desk. In most cases, the system administrator is a registered nurse or a nurse practitioner. The input of patient information has followed the retrieval of the patient and drug information. It is done by a doctor or a physician. The physician then requests for a prescription. This occurs still in the EHRs. After receiving the prescription, the doctor makes a decision whether it is the best combination. If there is a discrepancy between the prescription and the patient/ drug information, then they go back to the request prescription step. If the combination is best, the pharmacist gives the medication and updates the system. Then medication reconciliation occurs. The workflow gap exists only at the choosing of the best combination prescription. This comes from a system which is not updated on the patient and drug information.

I made a complete overhaul of my Visio model draft. This was based on the critiques from my colleagues. The draft lacked start and end of process shapes. They are supposed to be oval or round (Helmers, 2011). I changed the inappropriate shapes I had used in the draft. I also removed some of the diamond shapes which were not meant to represent decision-making. Lastly, I included swimlanes which were not present in my Visio draft. They indicate individuals responsible for different steps in the workflow (Helmers, 2011). 

Summary

In this paper, I was able to give an explanation of the gap analysis results, an analysis of the issues identified in the EHRs, description of the final Visio model and the changes made. Gap analysis work out the size, and shape, of the strategic tasks to be undertaken in moving from its present state to a desired one.

Visio Model for Current-State Workflow

System Administrator	Doctor	Pharmacist
Admission Medication orders/Medication History Compare Discrepancies identified No No further action required	Intentional discrepancies Yes Ask prescriber yes No intentional discrepancy Reconcile	Document

References

Agrawal, A. (2009). Medication errors: prevention using information technology systems. British journal of clinical pharmacology, 67(6), 681-686. DOI: 10.1111/j.1365-2125.2009.03427.x

Bowman, E. H., Flood, K. L., & Arbaje, A. I. (2014). Models of Care to Transition from Hospital to Home. In Acute Care for Elders (pp. 175-202). Springer New York.

Campbell, E. M, Guappone, K. P., Sittig, D. F., Dykstra, R. H., & Ash, J. S. (2009). Computerized provider order entry adoption: Implications for clinical workflow. Journal of General Internal Medicine, 24(1), 21–26. DOI: 10.1007/s11606-008-0857-9

Dennis, A., Wixom, B. H., & Roth, R. M. (2015). Systems analysis and design (6th ed.). Hoboken, NJ: Wiley.

Helmers, S. (2011). Microsoft Visio 2010 step by step. Sebastopol, CA: O’Reilly.

Unertl, K. M., Weigner, M. B., Johnson, K.B., & Lorenzi, N. M. (2009). Describing and modeling workflows and information flow in chronic disease care. Journal of American Medical Informatics Association, 16(6), 826–836. DOI: http://dx.doi.org/10.1197/jamia.M3000 

Wright, A., Sittig, D. F., Ash, J. S., Feblowitz, J., Meltzer, S., McMullen, C., ... & Evans, R. S. (2011). Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. Journal of the American Medical Informatics Association, 18(3), 232-242. DOI: http://dx.doi.org/10.1136/amiajnl-2011-000113