POST 1 JACQUELINE Type 2 Diabetes Mellitus presents a significant morbidity and mortality burden

POST 1

JACQUELINE

Type 2 Diabetes Mellitus presents a significant morbidity and mortality burden in the United States and African Americans (AA) experience a disproportionate burden of the disease. However, few studies have studied the effectiveness of a culturally tailored diabetes Self-Management education intervention. Low adherence rates are problematic in the AA population, and there is a gap in knowledge on how best to educate this population to help mitigate the effects of Type 2 Diabetes.        

The target population most appropriate for this research study is the Adult African American diagnosed with Type 2 Diabetes. So what is the impact of Culturally focused Diabetes Self-Management Education on Health Outcomes in the African American Type 2 diabetic? 

The study’s purpose is to explore the effect of a culturally tailored DSME intervention on self-efficacy and diabetes-related outcomes among African Americans.

What are the challenges of obtaining a sample from this population? How could you address those challenges?

According to Grove (2017), sampling involves selecting a group of people, events, behaviors, or other elements to conduct a study. There are significant challenges in obtaining a sample from the adult diabetic population, especially with COVID-19 precautions. Many patients at our local primary care facilities are using Telemedicine access for visits during the pandemic, so there are fewer opportunities to approach participants regarding interest in taking part in a research study, 

Also, another challenge is that face-to-face interviews cannot currently be conducted efficiently, and participants may shy away from this method. Overcoming some of the difficulties could include telephone interviews, video interviews, telephone surveys, seeking provider input on possible participants, and using online surveys to collect data. 

What approach would you use to collect data from the sample? Provide a rationale for the approach you choose based on this week’s Learning Resources.

Data collection tools are “procedures or instruments used to guide the collection of data in a standardized fashion” (Polit & Beck, 2010, p. 716). To complete this study, we will use a pre-, and post-tests (questionnaires) research design; baseline and post-test assessments will be completed, with a convenience sample of Type 2 Adult African American diabetic participants. In the convenience sampling method, subjects are included in the study because they happen to be in the right place at the right time. Available subjects are entered into the study until the desired sample size is reached. 

A quantitative methodology, which focuses on data collection, will be used to complete the research study. Quantitative research is ‘Explaining phenomena by collecting numerical data analyzed using mathematically based methods, particularly statistics’ (Aliaga & Gunderson,2000 ).

This researcher will use Data collected from the pre-and post-education questionnaires to determine the effectiveness of a culturally focused diabetes self-management education on diabetes knowledge and self-efficacy, self-management changes such as blood glucose self-monitoring improved hemoglobin A1C. 

Similarly, Face-to-Face interviews or phone interviews to collect data may be used; however, most people are more truthful while responding to questionnaires because their responses are anonymous. Additionally, questionnaires can simplify and quantify a person’s behaviors, knowledge base, and attitudes. 

PICOT Question :

In African Americans with Type 2 Diabetes, does a culturally tailored diabetes education, including patient-specific dietary and lifestyle modifications, reduce A1c levels, improve self-efficacy and self-management  after six months versus a control group of African Americans? 

Reference:

 Aliaga, M., & Gunderson, B. (2000). Introduction to Quantitative research. Doing Quantitative

 Research in Education with SPSS. Thousand Oaks, CA: Sage Publications, 1-11.

Grove, S.K. (2017). ‘Sampling,’ in Gray, J.R., Gove, S.K. & Sutherland, S. (8th ed.) Burns and 

Grove’s the practice of nursing research: Appraisal, synthesis, and generation of 

Evidence. St. Louis, MO. Saunders Elsevier. pp. 329- 362.

Polit, D. F., & Beck, C.T. (2010). Nursing research: Principles and methods (8th ed.).

 Philadelphia, PA: Lippincott Williams & Wilkins: Wolters Kluwer Company.

POST 2

JELDA

The sampling component is an important part of the research process that needs to be carefully thought out and clearly described (Grove, 2017). It involves selecting a group of people, events, behaviors, or other elements with which to conduct a study (Grove, 2017). With regard to my research problem of hand hygiene, some researchable populations would be NICU nursing staff and department/unit physicians. The most appropriate of the two for my research study would be NICU nursing staff due to them being the more accessible population. Meaning I have the most reasonable access to them. 

Sample Challenges

One of the challenges of obtaining a sample from the NICU nursing staff is the size of the  research team. According to (Gray, 2017), the larger, more complex the study, the less likely it is that the study will be implemented by one person. To address this issue, I could add a few people to create a small research team for the study. Another challenge would be time factors as different NICU nursing staff work on different shifts. Per (Gray, 2017), The number of days, weeks, or months required in order to obtain enough subjects for the research is a more difficult prediction, because unforeseen circumstances may make things like securing access to subjects, obtaining consent, and collecting data more of an extended process that originally envisioned. To address this issue, I would utilize the additional research team members to assist with covering the various staff on different shifts and/or provide an online option for the study.

Data Collection

A considered data collection approach I would take is via the online method. Online services can be easy to use for both the researcher and study participants (Gray, 2017). Computer software packages developed by a variety of companies enable researchers to provide instruments and other data collection forms online to potential subjects. Online survey software makes it easy to download data directly into computer packages and greatly reduces the potential for data entry mistakes which is certainly an advantage (Handscomb & al, 2016).

References

Gray, J. R. (2017). Collecting and managing data. In J. R. Gray, & e. al, Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.) (pp. 493-518). St. Louis, MO: Saunders Elsevier.

Grove, S. K. (2017). Sampling. In J. R. Gray, & e. al, Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.) (pp. 329-362). St. Louis, MO: Saunders Elsevier.

Handscomb, L., & al, e. (2016). Online data collection to evaluate a theoretical cognitive model of tinnitus. American Journal of Audiology, 25(3S), 313-317.

POST 3

LOLADE

 Evaluate the challenges that health care organizations may face when sharing data across systems

            The need for sharing of information across health departments is important in enabling quality improvement initiatives in a health organization. However, health organizations experience a number of challenges in sharing data across systems. One of the challenges is the lack of a consistent approach to identifying patients across the different systems of health. The sharing of information requires the use of patient information for identification purposes. Health organizations face the challenge of adopting a uniform approach to patient identification that will minimize violation of privacy and confidentiality of the patients’ data. As a result, the lack of consistent approach to patient identification across systems makes it difficult to share information across systems. The other challenge is the lack of interoperability in the health information systems used in different organizations. The lack of interoperability implies that health organizations have to manipulate the data prior to sharing among them (Stegemann & Gersch, 2019). The consequences include the loss of data meaning and integrity, which affect the coordination efforts between organizations.

Using your professional experience and/or information gathered through research, provide at least two specific examples of interoperability challenges

            Interoperability challenges affect the sharing of information across healthcare systems. One of the interoperability challenges that I have experienced in my practice is financial barriers. Health organizations have to incur significant costs in acquiring new systems and training its staffs to acquire the skills needed in addressing interoperability challenges. The second challenge is the lack of trust. Sharing patient information predisposes the patient and organizational data to loss (Oyeyemi & Scott, 2018). Consequently, health organizations do not show the needed dedication to addressing the challenges due to the ethics and legal implications of data sharing.

                            Propose at least two strategies an health care organization might implement to address interoperability challenges

            One of the strategies that healthcare organizations may implement to address interoperability challenges is the use of national patient identifiers. The use of national patient identifiers will improve the consistent reporting and sharing of patient information by healthcare organizations. The second solution is the development and use of consistent standards of information sharing (Oyeyemi & Scott, 2018). An example is the adherence to the legal and ethical provisions of legislations such as HIPAA. 

References

Oyeyemi, A., & Scott, P. (2018). Interoperability in health and social care: Organisational issues are the biggest challenge. BMJ Health & Care Informatics, 25(3), 196–197. https://doi.org/10.14236/jhi.v25i3.1024

Stegemann, L., & Gersch, M. (2019). Interoperability – Technical or economic challenge? It – Information Technology, 61(5–6), 243–252. https://doi.org/10.1515/itit-2019-0027

POST 4

RUTH

Week 3 Discussion: Controlled Terminology and Standards   

March 15, 2021

Ruth Novack

Current Challenges Sharing Data Across Systems

            Patients seek care in multiple healthcare outlets. An individual may have a primary physician they see in a clinic however, if that patient is admitted to the hospital that specific primary may not have privileges lending to the necessity of shared data. Interoperability of these systems allows the uninterrupted and safe care for the patient. Computers work through numerical coding and software such as the Systematized Nomenclature of Medicine Clinical Terminology (SNOMED CT)  and Unified Medical Language System (UMLS) that allow the recognition of phrasing and coding to interact with the electronic health record (EHR) (Truran D et al., 2010). Challenges come into play when organizations do not select standardized vocabulary sets or phrases. Standardizing vocabulary lends to more robust data storage and mining when extracting knowledge. The American Nurses Association (ANA)  (2018) supports the use of recognized terminologies and has developed position statements based on their use in the EHR.   

Two Examples of Interoperability Challenges

 My organization utilizes Radiant, EPIC, and CUPID. Although these patients are receiving care in one organization these separate entities do not flow to each other creating the need for separate platforms. With these separate platforms, two charts must be created for one surgical encounter. One is created for the surgical services department and the other appointment is order-driven to radiology (Radiant). This has created multiple discrepancies in the documentation. The providers are working in the appointment chart that is radiant however, nurses are documenting in the EPIC EHR and unable to visualize any orders from the provider. The providers are having to move out of the appointment chart and into the EPIC chart to place orders. 

            Another situation is that nursing units utilize different descriptive terms. This creates difficulty when attempting to promote consistency in patient documentation and billing. When the nursing description does not match the appropriate billing code the disparities may create a chain reaction to deny reimbursement. This situation was created when our patient care center (PCC) took over the procedural sedation of the cardioversion. The cardiologist is working within their system and although the patient was an outpatient they have to log in as an inpatient provider in order to properly place post-procedure orders, after visit summary, and discharge order. The CUPID platform will assist in this migration of data (Change Healthcare Cardiology, n.d.).

Propose Two Strategies to Mitigate Challenges

 CUPID platform will assist in mitigating the challenges now faced for providers and nursing in promoting a more seamless transition for record-keeping. When the appointments are made if they do not attach PCC as an ancillary department the thin log will not populate to the surgical census and therefore only one chart is present to work in. Having the hospital develop a standardized nursing language based on already validated and reliable vocabulary will assist in mitigating loss of revenue with billing mishaps. 

References

ANA. (2018). Inclusion of Recognized Terminologies Supporting Nursing Practice within Electronic Health Records and Other Health Information Technology Solutions—ANA Position Statement. ANA. https://www.nursingworld.org/practice-policy/nursing-excellence/official-position-statements/id/Inclusion-of-Recognized-Terminologies-Supporting-Nursing-Practice-within-Electronic-Health-Records/

Change Healthcare Cardiology. (n.d.). Change Healthcare. Retrieved March 13, 2021, from https://info.changehealthcare.com/cardiology

Truran D, Saad P, Zhang M, & Innes K. (2010). SNOMED CT and its place in health information management practice. Health Information Management Journal, 39(2), 37–39. https://doi.org/10.1177/183335831003900206