NUR707 WEEK49 ASSIGNMENT 3
Week 4 Discussion
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- In your own words define the terms confidentiality and anonymity. Include an example of each in your response.
Confidentiality can be defined as the need to secure and protect information from unauthorized third party without the consent or permit from individuals who owns health-related information (hhs.gov, 2021). In clinical practice, confidentiality means that healthcare professionals should not disclose or share patients’ health information and personal data which includes their name, contact number, address (hhs.gov, 2021; Merriam-Webster, 2021). When applied to my DNP capstone project, a good example of confidentiality includes taking it a habit not to share or disclose all patients’ name, telephone number, e-mail, bank account number, social security number, license number, IP address(es), biometrics, photo or home address to other people especially when there is no consent taken directly from the patient(s) (Menger et al., 2018; HIPAA Journal, n.d.).
In support of confidentiality law, the definition of anonymous includes making each individual’s identity unknown and unrecognizable to the public (Merriam-Webster, 2021). By not disclosing a person’s real name or anything that can be used to identify a person, the privacy of people who agree to participate in a case study can be protected as required by Health Insurance Portability and Accountability Act of 1996 (HIPAA) law (CDC, 2021; HIPAA Journal, n.d.). When applied to my DNP capstone project, a good example of anonymous includes the use of unique code number which could represent each individual research participant instead of using their real name (Boyd et al., 2007).
- Discuss how data retrieved from a health system database can be de-identified prior to viewing. Identify the role of an honest broker if one is used in this process. Describe how de-identification supports privacy and confidentiality.
There are ways to de-identify data retrieved from health system database. For instance, Pal et al. (2014) and Kushida et al. (2012) discussed algorithm such as the use of hash function in preventing retrieval or third party access of patients’ identity when trying to retrieve data from health system database. Using a single integer, Kushida et al. (2012) explained that the hash function will assign anonymous code when retrieving patients’ health information (Kushida et al., 2012). According to Pantazos et al. (2017, p. 293), other ways to de-identify data retrieved from health system database include the use of “machine learning”, “natural language processing (NLP)”, and “named entity recognition”. Without making any form of manual intervention, Pantazos et al. (2017) explained that different algorithm techniques such as the “machine learning”, “natural language processing (NLP)”, and “named entity recognition” are useful when it comes to simplifying the process of de-identification of patients data available in a health system database. By replacing all personal identifiers found in patients’ health record or free-text notes into non-personal identifiers, all these three (3) algorithm techniques that was mentioned by Pantazos et al. (2017) are equally useful in terms of making patients’ identifiers anonymous.
Similar to algorithm such as hash function, Boyd et al. (2007) explained that the honest broker can also be used to de-identify patients’ data that can be retrieved from a health system database. Basically, the main role of honest broker in de-identification process is to get rid of all data elements that can be considered as identifiable information (Boyd et al., 2007). By removing identifiable data in patients’ health record(s), patients’ privacy and right to confidentiality can be observed and maintained (Pantazos et al., 2017; Kushida et al., 2012; Boyd et al., 2007). For instance, when performing data transfer between clinical to research system, the honest broker will allocate a distinctive ID code to each patient record found within health database system (Boyd, et al., 2007). By disregarding patients’ personal information and keeping it anonymous at all times, the risks of violating HIPAA law particularly in terms of protecting patients’ sensitive health records can be avoided (CDC, 2021; HIPAA Journal, n.d.).
The HIPAA law aims to protect patients’ sensitive health-related data from being disclosed to unauthorised third party without their knowledge or consent (CDC, 2021). With this in mind, de-identification supports privacy and confidentiality by removing patients’ identifiable information from open health database system (Pantazos et al., 2017; Kushida et al., 2012; Boyd et al., 2007). This way, unauthorised third party who will try to hack or access patients’ information from open health database system will not be able to clearly identify patients’ sensitive data (Menger et al., 2018; HIPAA Journal, n.d.).
- Identify at least two strategies the DNP prepared clinicians could use to ensure the accuracy of data being collected in the case study.
Similar to quality control, Yamanaka et al. (2016) stated that observing quality assurance is necessary to ensure that researchers could preserve data integrity in research studies. By strictly following a standardised data collection and measuring method, researchers who uses accurate data could come up with a valid and reliable research study result (Yamanaka et al., 2016).
In practice, it is the duty and responsibility of DNP prepared clinicians to ensure and maintain the accuracy of data gathered from a research/case study. With this in mind, a couple of strategies DNP prepared clinicians can use to guarantee data accuracy is to observe quality assurance and quality control during the entire data gathering and data analysis process (Yamanaka et al., 2016; ORI, n.d.).
For example, allocating a distinct code number in Ganey Press survey questionnaire should be considered a part of quality assurance. By allocating a distinct code number, DNP trained clinicians will have the opportunity to collect data with integrity. Using the allocated code number, DNP trained clinicians can verify data accuracy when tallying survey result. To control the quality of data gathered in Ganey Press survey questionnaire, DNP prepared clinicians should locate questionnaires with errors (i.e. checking two (2) or more boxes in each survey question). By removing survey questionnaires with doubtful answer, DNP prepared clinicians can maintain data accuracy.
References
Boyd, A., Hosner, C., Hunscher, D., Athey, B., Clauw, D., & Green, L. (2007). An ‘Honest Broker’ mechanism to maintain privacy for patient care and academic medical research. International Journal of Medical Informatics, 76(2007), 407-411, http://doi.org/10.1016/j.ijmedinf.2006.09.004.
CDC. (2021). Health Insurance Portability and Accountability Act of 1996 (HIPAA), https://www.cdc.gov/phlp/publications/topic/hipaa.html.
hhs.gov. (2021). Summary of the HIPAA Privacy Rule, https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html.
HIPAA Journal. (n.d.). What Information is Protected Under HIPAA Law?, https://www.hipaajournal.com/what-information-is-protected-under-hipaa-law/.
Kushida, C., Nichols, D., Jadrnicek, R., Miller, R., Walsh, J., & Griffin, K. (2012). Strategies for de-identification and anonymization of electronic health record data for use in multicenter research studies. Medical Care, 50(7), S82-S101.
Menger, V., Scheepers, F., van Wijk, L., & Spruit, M. (2018). DEDUCE: A pattern matching method for automatic deidentification of Dutch medical text identification of Dutch medical text. Telematics and Informatics, 35(2018), 727-736, http://dx.doi.org/10.1016/j.tele.2017.08.002.
Merriam-Webster. (2021). anonymous, https://www.merriam-webster.com/dictionary/anonymous.
ORI. (n.d.). Data collection, https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/dctopic.html
Pal, D., Chen, T., Zhong, S., & Khathavath, P. (2014). Designing an algorithm to preserve privacy for medical record linkage with error-prone data. JMIR Medical Informatics, 2(1), e2, http://doi.org/10.2196/medinform.3090.
Pantazos, K., Lauesen, S., & Lippert, S. (2017). Preserving medical correctness, readability and consistency in de-identified health records. Health Informatics Journal, 23(4), 291-303, http://doi.org/10.1177/1460458216647760.
Yamanaka, A., Fialkowski, M., Wilkens, L., Li, F., Ettienne, R., Fleming, T., . . . Novotny, R. (2016). Quality assurance of data collection in the multi-site community randomized trial and prevalence survey of the children’s healthy living program. BMC Research Notes, 9(432), https://doi.org/10.1186/s13104-016-2212-2.
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Elliott Cowart
May 23 at 12:44
Veronica,
Thank you for your post. This is Elliott, not Quincy. As DNP-prepared nurses finish their education preparation, they are obligated to mentor and teach new and those who have been in nursing for many years in providing the latest evidence-based practice (EBP) to provide our patients with the best and latest care available.
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Veronica Singfield
May 22 at 23:31
Hello Quincy,
Great post. I agree with your position that DNP-trained nurses have a primary responsibility of ensuring the accuracy of data in projects they lead and peer review. Additionally, as DNP-prepared nurses advance to leadership roles in various parts of the healthcare industry, I believe their influence in data interpretation and making data-driven decisions with also increase.
DNP-trained nurses also set the example for new graduate and seasoned nurses, particularly in the utilization of tools such as the electronic health record (EHR). Nurses provide most of the patient-centered documentation that is entered into the EHR and their documentation is essential to patient safety, data integrity, and accuracy (Glassman, 2017). Nurses today, particularly those that further specialize in informatics, have fostered innovation to improve the utility of the EHR by incorporating “smart-phrases” and using enhanced data recognition and artificial intelligence for natural language processing in the EHR (Glassman, 2017).
Great job!
Reference
Glassman, K. (2017, November ). Using data in nursing practice. https://www.myamericannurse.com/wp-content/uploads/2017/11/ant11-Data-1030.pdf
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Week 5 Discussion
Elliott Cowart posted May 24, 2021 14:48
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Discuss the importance of selecting a benchmark in relation to evaluating and presenting project outcomes
To develop hospital performance in patient satisfaction, DNP-trained clinicians are highly encouraged to conduct internal and external performance comparisons in benchmarking, searching for best practice(s) in quality improvement (Mull et al., 2019; Brown et al., 2010; Billings et al., 2001). In practice, this can be done by encouraging DNP trained clinicians to compare the hospital’s past and current patient satisfaction performance internally and externally with other similar hospitals’ patient satisfaction performance (Feibert et al., 2019; Mull et al., 2019; Wilson & Nathan, 2003; Brown et al., 2010).
Importance of Selecting a Benchmark
In general, there are several advantages to evaluating and presenting the project outcome using a benchmark. First of all, benchmarking will help increase healthcare professionals’ knowledge and understanding regarding institutional performance in healthcare quality and patient satisfaction (Brown et al., 2010). In support of knowledge transfer within and outside the healthcare institution, benchmarking will allow the healthcare team to share and discuss the best practice(s) to increase patient satisfaction in healthcare services (Mull et al., 2019; Billings et al., 2001). By implementing external performance comparison in benchmarking, DNP-trained clinicians can identify and adopt the best practice(s) of other similar healthcare organizations (Feibert et al., 2019; Billings et al., 2001).
Overall, benchmarking will help improve the hospital’s best practice(s) as the healthcare professionals try to reach the optimal level to increase their patient satisfaction performance (Brown et al., 2010; Billings et al., 2001). In general, the purpose of creating a benchmark is to establish a baseline point of comparison which makes it possible for DNP-trained clinicians to determine if their proposed change initiative can lead to better patient satisfaction and quality outcome (Wilson & Nathan, 2003; Billings et al., 2001). Through evidence-based research, selecting a benchmark is essential because it serves as a standardized measurement when measuring hospitals’ healthcare quality and patient satisfaction (Brown et al., 2010). Aside from setting goals healthcare institutions aim to reach, benchmark metrics could facilitate DNP-trained clinicians’ ability to accurately measure and evaluate the project outcomes (Mull et al., 2019; Brown et al., 2010; Wilson & Nathan, 2003).
Examples in Relation to My Own Project
Evidence shows that the first step in benchmarking is to identify what benchmark to use to evaluate the project outcome, followed by defining the mapping process, creating the Ganey Press survey questionnaire, which is composed of a 5-point Likert scale, conduct a pilot study, and measure performance (Billings et al., 2001; Czarnecki, 1996). Before conducting external performance comparison in benchmarking, Billings et al. (2001) suggest that DNP-trained clinicians should first examine and understand internal work procedures before finally searching for the best practice and matching those identified best practices with other similar organizations.
It is given that the proposed change initiative in my project is to use interactive video interpreters in patient teaching and shared decision-making when selecting the best pain management option for non-English patients who are expectant mothers. To measure and record internal benchmarks for evaluating and presenting the project outcome, the proposed method is to create and use a Ganey Press survey questionnaire to measure patient satisfaction in patient teaching and shared decision-making without using an interactive video interpreter. It means that the actual score in the Press Ganey survey questionnaire without the use of
video interactive interpreter will serve as the benchmark to evaluate the project outcome.
Now that a benchmark has been established, DNP-trained clinician(s) should apply video interactive interpreters when delivering patient teaching and making shared decision-making in patients’ pain management options. By comparing Ganey Press score without and with the use of interactive video interpreter, DNP trained clinician(s) could identify gap(s) in labor and delivery (L&D) departments’ patient teaching and shared decision-making performance (Mull et al., 2019; Billings et al., 2001; Bagchi, 1996). Upon identifying the organizational performance gap, DNP-trained clinicians will have the opportunity to think of ways to improve the internal organizational process (Mull et al., 2019).
- Identify appropriate benchmarks (internal and external) that could be used to evaluate the outcomes of your study. Why did you select those benchmarks? Are the benchmarks realistic? Mathematically plausible?
By definition, the benchmark is a point of reference that can be used as a standard or basis in comparison to recent performance measurements (Wilson & Nathan, 2003). To evaluate the project outcome, the best and most appropriate way to get an internal benchmark is to create and use a Ganey Press survey questionnaire to measure patient satisfaction in patient teaching and shared decision-making without the use of an interactive video interpreter (Billings et al., 2001; Czarnecki, 1996). Serving as a benchmark in measuring the success rate of the project outcome, the main reason why I decided to measure patient satisfaction in patient teaching and shared decision-making without the use of interactive video interpreter using the Press Ganey style survey questionnaire is that this method will provide me with Press Ganey score before the implementation of the proposed change intervention. By comparing the Ganey Press score with and without using an interactive video interpreter, I can measure how well the proposed change initiative effectively increases patient satisfaction in both patient teaching and shared decision-making.
Due to close market competition, some hospitals treat their internal records on patient satisfaction ratings as highly confidential. Therefore, I decided to gather data directly from a highly reputable publisher. For instance, published in Australian Health Review, I will consider the patient satisfaction rating of Schultz et al. (2015) regarding the use of videoconferencing interpreters as an external benchmark for this project. According to Schulz et al. (2015), patient satisfaction using hospital-trained interpreters via videoconferencing is as high as 98%. It means that after implementing the proposed change initiative, there is a strong possibility that I could reach as high as 98% in the overall Press Ganey score.
To complete this study, only a small sample size, hopefully, 20-30 people, will be collected within a short period of six (6) months. For this reason, Feibert et al. (2019) said that it is not advisable to use a mathematical programming method similar to Data Envelopment Analysis (DEA) when measuring improvements in patient satisfaction[1].
References
Bagchi, P. (1996). Role of benchmarking as a competitive strategy: the logistics experience. International Journal of Physical Distribution & Logistics Management, 26(2), 4-22, https://doi.org/10.1108/09600039610113173.
Billings, D., Connors, H., & Skiba, D. (2001). Benchmarking Best Practices in Web-Based Nursing Courses. Advance Nursing Science, 23(3), 41-52, http://doi.org/ 10.1097/00012272-200103000-00005.
Brown, D., Donaldson, N., Bolton, L., & Aydin, C. (2010). Nursing-Sensitive Benchmarks for Hospitals to Gauge High-Reliability Performance. Journal for Healthcare Quality, 32(6), 9-17, http://doi.org/ 10.1111/j.1945-1474.2010.00083.x.
Czarnecki, M. (1996). Benchmarking: a data-oriented look at improving health care performance. Journal of Nursing Care Quality, 10(3), 1-6, https://pubmed.ncbi.nlm.nih.gov/8634465/.
Feibert, D., Andersen, B., & Jacobsen, P. (2019). Benchmarking healthcare logistics processes – a comparative case study of Danish and US hospitals. Total Quality Management, 30(1), 108-134, https://doi.org/10.1080/14783363.2017.1299570.
Mull, M., Keiffer, G., Fulmore, J., Roberts, P., & Nimon, K. (2019). Developing a benchmarking survey for academic members of an international academy. How to create and refine tools and collect data for measuring performance. Planning for Higher Education Journal, 47(2), https://go.gale.com/ps/anonymous?id=GALE%7CA654225991&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=07360983&p=AONE&sw=w.
Schulz, T., Leder, K., Akinci, I., & Biggs, B.-A. (2015). Improvements in patient care: videoconferencing to improve access to interpreters during clinical consultations for refugee and immigrant patients. Australian Health Review, 39(4), 395-399, https://doi.org/10.1071/AH14124.
Wilson, A., & Nathan, L. (2003). Understanding benchmarks. Home Healthcare Nurse, 21(2), 102-107, http://doi.org/ 10.1097/00004045-200302000-00008.
[1] In general, the accuracy of numeric measurements is highly dependent on the use of a large sample size (Feibert et al., 2019).
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Elliott Cowart
May 29 at 11:10
Veronica,
Thank you for your post. The concept of using interactive interpreters is not new, as prior to this we were accustomed to using telephone interpreters which had mixed results depending on the country or dialect the person was speaking. Many times we ran into trouble as there was not an interpreter available that spoke the same language or dialect our patient. During these times we were usually given a reference number and a promise they will call us back (usually several hours later) when there would be an interpreter available. This also occurs with the video interpreters, but not as often. At this moment I’m still working through the development of my benchmarks trying to perfect it/them.
Elliott
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Elliott Cowart
May 29 at 10:54
Walter,
Thanks for your post, your points were insightful and well thought out. The delivery of healthcare as far as pregnancy is concerned in many countries is slightly different than we practice here. Some of the pregnant patients we treat are and were healthcare practitioners in their primary country of origin. That being said, our healthcare practitioners here hear about and are educated in the various healthcare styles of other countries. Of course, those are the patients most fluent in English and capable of conversing on the variation of healthcare practices. The use of online interactive interpreters allows us to interact with patients across many different dialects and languages. Which ultimately will improve our patient satisfaction scores.
Elliott
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Walter Toller
May 29 at 10:07
Elliot, great job on your post and thank you for sharing with us. Benchmarking can be defined as the process of comparing business tasks and performance metrics in comparison with the industry’s best performers and industry best practices (Anderson et al., 2018). Benchmarking helps to increase the knowledge of healthcare professionals and can assist in creating goals that are achievable. While working towards that goal, healthcare professionals can discuss the best practices that are based on evidenced-based research. Benchmarking provides a standardized measurement that is used to assess how effective an intervention was. For my project, identifying an appropriate benchmark was very challenging due to the specific clinical problem I am addressing.
References
Andersson, E., Arfwidsson, O., & Thollander, P. (2018). Benchmarking energy performance of industrial small and medium-sized enterprises using an energy efficiency index: Results based on an energy audit policy program. Journal of cleaner production, 182, 883-895. https://doi.org/10.1016/j.jclepro.2018.02.027
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Veronica Singfield
May 28 at 22:02
Hello Elliot,
I appreciate your post. You selected an interesting project topic, interactive video interpreters. If I understand the utility referenced in your post correctly, the interpretation will occur via the live exchange between a specifically certified and fluent interpreter and the patient. We frequently use this type of engagement and technology at my facility. Initially, the setup with my patients and interpreters felt a bit forced, but once the dialog began, the exchange progresses quite effortlessly.
I am interested in learning more about your defined benchmarks as well. I notice that you are already navigating the challenges of confidentiality as you conduct your research. The benchmarking may have some unintended variation since your research data extends to other countries unless your study’s context is international. There is an element of complexity when examining healthcare delivery across nations; just as you mention, your study is broadened to the Australian Health Review. I imagine in addition to confidentiality issues; you’re also navigating the cultural differences between care delivery in America versus Australia. Reponen et al., (2019) explain that evidence-informed decisions based on limited or scarce literature on benchmarking should have the influence of contest carefully examined. The context could validate your research or bring to question the reliability and validity of the research if not well grounded. Kudos to you for choosing an ambitious topic, I’m looking forward to learning more about it.
Great post!
Reference
Reponen, E., Randall, T., Shortell, S., Blodgett, J., Juarez, A., Jokela, R., Mäkijärvi, M., Torkki, P. (2021). Benchmarking outcomes on multiple contextual levels in lean healthcare: A systematic review, development of a conceptual framework, and a research agenda. BMC health services research. https://doi.org/10.1186/s12913-021-06160-6
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Week 6 Discussion -Outcomes
Elliott Cowart posted Jun 1, 2021 06:05
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Brief Discussion of the EBP Problem and Chosen Intervention
The Healthcare sector faces many challenges and issues which call for the implementation of evidence-based practice (EBP) interventions to resolve or improve. Managing and reducing patients’ pain is a significant challenge clinicians face within critical healthcare settings (Lichtner et al., 2016). Patients hospitalized or admitted or receiving treatment and clinical care in labor and delivery units, critical care units, post-operative or surgical units, hospice care units, general wards, and other units experience moderate to severe pain (Lichtner et al., 2016). This pain needs to be assessed efficiently, relieved, and managed timely and appropriately to reduce patients’ pain and suffering, promote their quality of life, and enhance positive patients experiences.
The evidence-based practice problem of this plan to focus on is effective, timely, and regular patients’ pain assessment using validated tools like the Wong-Baker Face Pain Rating Scale (FPR-Scale) to improve pain management and decrease patients’ use of PRN medications. This chosen intervention will involve implementing regular pain assessment in hospital settings to measure and assess patients’ pain every shift to reduce patients’ excessive use of PRN medications. Pain is a common and pervasive symptom in critical care settings. Patients admitted in critical care units with chronic, acute, and terminal illnesses experience high levels of pain, which require evidence-based practice interventions to manage and relieve and thus improve the patients’ quality of life and outcomes (Fink, 2015). Sometimes healthcare practitioners such as nurses and physicians fail to assess patients’ pain regularly and promptly, leading to patients experiencing high pain levels.
Consequently, these patients request unscheduled PRN medications, which have adverse effects on their health and wellbeing. These effects include developing pain medication tolerance and increased risk of medication errors associated with unscheduled PRN medication prescription and administration (Oh et al., 2014). Other harmful or adverse effects of high PRN medication use include neurologic deterioration, excessive sedation, nausea, vomiting, pruritus, insufficient analgesia, and respiratory insufficiency (Vaismoradi et al., 2018). Besides, increased PRN medication use can lead to increased medical care costs for patients. Hence, regular, timely pain assessment every shift or before handover using validated Wong-Baker (FPR-Scale) is a necessary EBP intervention to improve pain management and patient pain outcomes like decreasing PRN medication use (Miró et al., 2016).
The Important Outcomes to Measure
In implementing the above described EBP intervention on implementing a validated pain assessment tool to measure or assess patients’ pain levels every shift or handover to improve pain management and patients’ pain outcomes, one important outcome to measure or evaluate is PRN medication use. When patients’ pain is not timely and adequately managed or controlled using appropriate pharmacologic and non-pharmacologic interventions, patients experience pain, suffering, and discomfort (Vaismoradi et al., 2018). As a result, they frequently call nurses to request PRN medications (Vaismoradi et al., 2018). The PRN prescription or medications stands for ‘pro re nata’ and refers to administering drugs that are not scheduled but instead prescribed and taken as needed (Oh et al., 2014). As mentioned earlier, these medications have negative health impacts on patients and need to be avoided. Hence, on implementing per shift pain assessment using the Wong-Baker (FPR-Scale), nurses, physicians, and other stakeholders in the facility need to measure the changes in PRN medication use.
The patient outcome on decreased use of PRN medication can be measured or assessed by reviewing the patient charts for a given period before and after or during the implementation of the intervention. The data to measure the outcome is available on the electronic health records patients’ charts and information. Nurses usually record any patient care or medication administered to patients. Therefore, patient charts from the electronic health records provide valid and reliable information and data to measure changes in patients’ use of PRN medications.
Benchmarks For the Intervention and Outcome
Internal benchmarks for the EBP project include increase commitment and enthusiasm by nurses to assess patients’ pain every shift using the Wong-Baker (FPR-Scale). Most healthcare facilities and units assess pain once per day or after a few days. However, this project aims to increase pain assessments from at least once a day to at least thrice a day. Typically, healthcare facilities have three 8-hour shifts per day. Therefore, if patients’ pain is assessed using the validated tool at the beginning of each shift, this would result in three pain assessments every day. Another internal benchmark is to decrease PRN medication use by 50 percent in the labor and delivery within the facility where this EBP intervention project will be implemented. Ultimately, this means that the PRN medication requests by patients every day will decrease by half.
This EBP project will improve pain management and patient satisfaction due to better pain management and patient pain outcomes. Hence, this project can increase a healthcare facility’s preference by patients and their families and friends. One external benchmark is to increase patient satisfaction by 20percent, measured by the increase in the number of new patients over twelve weeks of the intervention implementation
References
Fink, R. (2015). Pain assessment: The cornerstone to optimal pain management. Proceedings (Baylor University. Medical Center), 13(3), 236–239. https://doi.org/10.1080/08998280.2015.11927681
Lichtner, V., Dowding, D., & Allcock, N. (2016). The assessment and management of pain in patients with dementia in hospital settings: A multi-case exploratory study from a decision making perspective. BMC Health Serv Res 16(427). https://doi.org/10.1186/s12913-016-1690-1
Miró, J., Castarlenas, E., de la Vega, R., Solé, E., Tomé-Pires, C., Jensen, M. P., Engel, J. M., & Racine, M. (2016). Validity of three rating scales for measuring pain intensity in youths with physical disabilities. European Journal of Pain (London, England), 20(1), 130–137. https://doi.org/10.1002/ejp.704
Oh, S. H., Woo, J. E., Lee, D. W., Choi, W. C., Yoon, J. L., & Kim, M. Y. (2014). Pro Re Nata prescription and perception difference between doctors and nurses. Korean Journal of Family Medicine, 35(4), 199–206. https://doi.org/10.4082/kjfm.2014.35.4.199
Vaismoradi, M., Amaniyan, S., & Jordan, S. (2018). Patient safety and pro re nata prescription and administration: A systematic review. Pharmacy (Basel, Switzerland), 6(3), 95. https://doi.org/10.3390/pharmacy6030095
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Jennifer Clinton
June 6 at 21:05
Hello Elliott-
I would agree that pain is often not adequately assessed or managed in an acute care setting. Although I do not work inpatient at this time, I know from speaking with colleagues in the surgical realm that they struggle with their patients’ pain being assessed and treated appropriately. This can range from no pain medication being given to narcotics being administered over non-narcotic alternatives. When acute pain is not managed well, it can lead to chronic pain, which then leads to a host of additional complications for the patient (Wells et al., 2008). There are both physiological and psychological consequences of untreated pain. I think a huge part of addressing this problem is staff education. Pain is difficult to treat, due to the very personal and subjective nature to it. There is also a stigma associated with pain and, in my experience, an inherent disbelief of patient report and bias against patients complaining of higher levels of pain. I think starting with regular and consistent assessment of pain levels is a good step towards demystifying pain and pain treatment. One question I have regarding your project, are you looking specifically at decreasing the use of PRN narcotics or PRN pain medication in general? In my own experience in the acute care setting, non-narcotic medications are underutilized.
Reference
Wells, N., Pasero, C., & McCaffery, M. (2018, April). Improving the quality of aare through
pain assessment and management. Nih.gov; Agency for Healthcare Research and
Quality (US). https://www.ncbi.nlm.nih.gov/books/NBK2658/
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Elliott Cowart
June 6 at 19:59
Wakeelat,
Attempting to control the patient’s pain level is usually a difficult endeavor that many healthcare providers face. Many patients that report severe pain while in hospital settings resort to the scheduled or non scheduled prn doses of pharmacological medication prescribed by their providers with mild to moderate relief reported ( Fink, 2015). The problem for these patients is once they leave the hospital settings, their unresolved chronic pain is no longer being managed to their satisfaction as a result they have a tendency to try other drugs which they believe will ease their pain which leads them down the path of illegal opioid usage as you mentioned.
References
Fink, R. (2015). Pain assessment: The cornerstone to optimal pain management. Proceedings (Baylor University. Medical Center), 13(3), 236–239. https://doi.org/10.1080/08998280.2015.11927681
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Wakeelat Adeoye
June 4 at 22:08
Elliot,
You have raised a very poignant healthcare topic that affects almost everyone who develops any condition that requires the attention of a medical professional. According to the Center for Disease Control and Prevention, 50 million adults in the United States have chronic daily pain, with 19.6 million adults experiencing high impact chronic pain that interferes with everyday life or work activities (CDC, 2017). Pain amongst patients ranges from mild to extremely severe. According to a 2016 article by The Journal of Pain, more than 80% of patients who undergo surgical procedures experience acute postoperative pain. Approximately 75% of those with postoperative pain report the severity as moderate, severe, or extreme. As you’ve rightly mentioned, accurately accessing the extent of the pain in patients is one of the significant steps to helping a patient remedy it.
In addition to the Wong-Baker Face Pain Rating Scale (FPR-Scale), you mentioned for pain assessment, a Virtual Analogue Scale (VAS) can also be used to assess pain. According to (Ludger et al., 2017), A Virtual Analogue Scale (VAS) is a psychrometric measuring instrument designed to document the characteristic of disease-related symptoms severity in individual patients and use this to achieve a rapid classification of symptom severity and disease control.
You also make a great point about the need for patients suffering from severe pain to request unscheduled PRN medications. Unfortunately, their demands don’t typically end with PRN medications alone; their request could go further into opioids, heroin, and synthetic opioids just to alleviate their pain. According to the Department of Health & Human Services, it is imperative to ensure that patients with painful conditions can work with their health care providers to develop integrative pain treatment plans that focus on optimizing function, quality of life, and productivity while minimizing risks for opioid misuse and harm.
References
Centers for Disease Control and Prevention (CDC) (2017 Fentanyl, Opioid Overdose https://www.cdc.gov/drugoverdose/opioids/fentanyl.html
Department of Health & Human Services, USA (DHHS) (2019). Pain Management: Best Practices. https://www.hhs.gov/sites/default/files/pmtf-final-report-2019-05-23.pdf
Ludger, K., Karl-Christian, B., Tilo, B., Jean, B., Peter, H., Kirsten, J., … Oliver, P. (2107) Visual analog scales (VAS): Measuring instruments for the documentation of symptoms and therapy monitoring in cases of allergic rhinitis in everyday health care.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5288410/
Roger, C., Debra, B. G., Oscar, A. D., Lisa, W., Steven, J. W., & Christopher, L. W. (2016). Management of Postoperative Pain: A Clinical Practice Guideline from the American Pain Society, the American Society of Regional Anesthesia and Pain Medicine, and the American Society of Anesthesiologists Committee on Regional Anesthesia, Executive Committee, and Administrative Council. The Journal of Pain.https://www.ajicjournal.org/article/S0196-6553(19)30314-1/fulltext
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Week 7 Discussion – Statistical Analysis
Elliott Cowart posted Jun 8, 2021 11:44
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NUR 707 Week 7: Statistical Analysis
To measure patient satisfaction in healthcare services, Press Ganey uses a survey questionnaire in data gathering (Frezza, 2020; Nelson & Watson, 2012). To measure patient satisfaction in patient teachings and shared decision making on labor pain management option(s), a Press Ganey survey questionnaire was exclusively designed to measure patient satisfaction at Long Island Jewish Forest Hills hospital. Based on the Press Ganey score, healthcare professionals working at this hospital could find ways to further improve patients’ overall experience when deciding for their preferred labor pain control method (Frezza, 2020). (See Appendix I – Sample of 5-point Likert Scale Survey Questionnaire on page 7)
In reference to the survey questionnaire, specific metrics applied in measuring project performance include patient satisfaction on health teachings and patient satisfaction with regards to healthcare professionals’ ability to provide the patients with the opportunity to participate in shared decision making and the overall patient satisfaction with regards to service quality they received from the healthcare professionals. To determine if the change implementation plan is effective in terms of addressing the clinical problem, descriptive statistical analysis has to be performed in each of these three (3) metrics.
For some unknown reason, it is easier to interpret scores in percentage (i.e. ranging from 0% to 100%) than scores in a 5-point scale (i.e. scales of 1 to 5) (Press Ganey, 2014). For this reason, DNP-trained clinicians should first convert patients’ responses on a 5-point survey scale (i.e. scale of 1 to 5) to percentage score way before computing the actual mean scores. As such, Table I summarizes the conversion table that will be used in converting patients’ responses on a 5-point survey scale (i.e. scale of 1 to 5) to a percentage score. (See Table I – Conversion of Press Ganey Scale to Press Ganey Score on page 3)
Table I – Conversion of Press Ganey Scale to Press Ganey Score
| Very Good | Good | Fair | Poor | Very Poor | |
| Scale | 5 | 4 | 3 | 2 | 1 |
| Score | 0% | 25% | 50% | 75% | 100% |
Source: Press Ganey, 2014, p. 3
Overall, the process of conducting a statistical analysis is necessary when it comes to writing the actual performance summary report. To do so, DNP-trained clinicians should compute for the “mean score”, “mean score percentile rank”, and “score rank” (Press Ganey, 2014; Press Ganey, 2013, p. 1). For example, using excel spreadsheet formula such as “=AVERAGE(array of numbers)”, DNP-trained clinicians can use the formula to compute for the mean score on patient satisfaction (Indeed Editorial Team, 2021). Another way to compute the mean score using Excel spreadsheet is to choose the AVERAGE from the dropdown menu list of formulas (Indeed Editorial Team, 2021).
Percentile ranking, when applied to mean score, is merely a strategy that focuses on assigning a series of numbers equally into different parts such that the median score in the database will be the 50th percentile (Press Ganey, 2014). Overall, the percentile ranking in relation to the mean score values simply tells us the proportion of the Press Ganey scores which could fall higher or lower than the hospital’s Press Ganey score (ibid). Totally different from the Press Ganey scale score, numeric information with regards to percentile ranking is useful in terms of letting the DNP trained clinicians to know where exactly does the labor and delivery (L&D) department stand in terms of being able to satisfy the patients in relation to the median score found on the database. For instance, 70 in percentile rank means that the L&D department is in the 70th percentile for a particular item (i.e. patient satisfaction in patient teaching, shared decision
making, and overall satisfaction in healthcare quality service).
In general, the mean score in descriptive statistics is similar to getting the average score straight from each question available in the survey questionnaire whereas the score ranking method is all about arranging the Press Ganey score percentage from highest to lowest (Press Ganey, 2013). In practice, the highest percentage score will get the first rank, and that the second-highest percentage score will get the second rank. With this in mind, the lowest percentage score will get the lowest rank (Press Ganey, 2014).
To improve the clinical problem with regards to healthcare professionals’ ability to satisfy patients in terms of patient teaching and shared decision making, specific area or item with the lowest score or lowest rank should be given priority and more attention particularly with regards to what the healthcare professionals can do to improve patient satisfaction in that specific area (Press Ganey, 2014). Using the mean score percentile ranking method, DNP trained clinicians would know which specific part or area in the change intervention plan to improve (ibid).
In reference to the sample survey questionnaire, Table II presents a hypothetical example of a situation coming from at least five (5) patients. As such, quantitative data shown in Table II illustrates how DNP-trained clinicians intend to compute the mean score on a quarterly basis. In reference to Table II, Set A and Set B score 85.0 which is lower than Set B’s score of 87.50. With this in mind, healthcare professionals working at the hospital should priorities Set A which is to improve patient satisfaction on health teachings, and Set B which is to improve the overall patient satisfaction with regards to the service quality they received from the healthcare professionals. (See Table II – Sample Computation of Mean Score on page 5)
Table II – Sample Computation of Mean Score
| Patient | A | B1 | B2 | B3 | B4 | C | Set A | Set B | Set C | Overall |
| 1 | 75 | 100 | 75 | 100 | 100 | 100 | 75 | 93.75 | 100 | 89.58 |
| 2 | 100 | 75 | 75 | 75 | 100 | 75 | 100 | 81.25 | 75 | 85.42 |
| 3 | 75 | 75 | 100 | 100 | 75 | 75 | 75 | 87.50 | 75 | 79.17 |
| 4 | 100 | 100 | 100 | 75 | 75 | 75 | 100 | 87.50 | 75 | 87.50 |
| 5 | 75 | 100 | 100 | 75 | 75 | 100 | 75 | 87.50 | 100 | 87.50 |
| Mean Score | 85.00 | 87.50 | 85.00 | 85.83 | ||||||
References
Frezza, E. (2020). Patient-Centered Healthcare. Transforming the Relationship Between Physicians and Patients. Routledge/Productivity Press.
Indeed Editorial Team. (2021, March 22). How To Calculate Mean in Excel and Why It’s Important, https://www.indeed.com/career-advice/career-development/how-to-calculate-mean-in-excel#:~:text=To%20find%20the%20mean%20in,A%2C%20rows%20two%20through%2020.
Nelson, J., & Watson, J. (2012). Measuring caring. International research on caritas as healing. Springer Publishing Company LLC.
Press Ganey. (2013). Scoring Quick Guide for Quick Reports. Rank, Top Box or Mean Score?, https://helpandtraining.pressganey.com/lib-docs/default-source/ip-training-resources/Scoring_Quick_Guide_for_Quick_Reports.pdf?sfvrsn=0
Press Ganey. (2014). Guide to interpreting. Part 2: statistics, https://helpandtraining.pressganey.com/lib-docs/default-source/ip-training-resources/guide-to-interpreting—part-2.pdf?sfvrsn=2#:~:text=Each%20patient%20has%20a%20score,of%20that%20patient’s%20section%20scores.
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Cowart Week 8 DF
Elliott Cowart posted Jun 16, 2021 22:53
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Descriptive statistics is the term that assists the students or researchers in analyzing collected data from the field and acquires knowledge to calculate some variables like mode, variance, and mean. Furthermore, descriptive information helps individuals measure the demographic data, examine the survey responses, and give a detailed summary of the results (Salkind & Frey, 2020). . Furthermore, descriptive information helps individuals measure the demographic data, examine the survey responses, and give a detailed summary of the results (Salkind & Frey, 2020). Descriptive statistics provides an overview and analysis of different descriptive information and their importance in data assessment. For instance, the mean, which is the core measure of the central tendency, is used to calculate or determine the normal value in the statistics sample. The mode indicates the utmost recurring value in a data set, while the median represents the middle value from lowest to highest in a data arrangement. The central tendency clarifies or assesses the overall responses of the data in a population (Salkind & Frey, 2020).
Salkind & Frey (2020) provides an overview and analysis of different descriptive information and their importance in data assessment. For instance, the mean, which is the core measure of the central tendency, is used to calculate or determine the normal value in the statistics sample. The mode indicates the utmost recurring value in a data set, while the median represents the middle value from lowest to highest in a data arrangement. The central tendency clarifies or assesses the overall responses of the data in a population (Salkind & Frey, 2020).
The researcher can also illustrate the standard deviation factor and its significance in the data analysis. Standard deviation measures the variability or how far the data value or an item appears to fall from the center. The variance in the data illustrates different components and its aspect of the distribution. Also, the standard deviation calculates the average variance in the data set. It gives insight into the differences between the value in the statistics and the mean value of the same data information (Young & Wessnitzer, 2016). Furthermore, the standard deviation evaluates whether the response from the data is crucial and worth considering. The author also elaborates the ANOVA methods or modest variance analysis, which analyzes the data collected from the Stanford tool. The importance of using these test methods is to demonstrate the differences between the respondents and groups. Furthermore, the ANOVA analysis can also help validate an improvement acquired due to daily life management of high blood pressure (Salkind & Frey, 2020).
References
Salkind, N. J., & Frey, B. (2020). Statistics for people who (think they) hate statistics (1sted.). Sage Publications, Inc.
Young, J., & Wessnitzer, J. (2016). Descriptive statistics, graphs, and visualization. In Modern statistical methods for HCI (pp. 37-56). Springer, Cham.
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Quincy Woods
June 18 at 15:20
Elliott Cowart
Thank you for your post. Descriptive statistics is important for my project because I will need to make charts to explain my data in simple terms. Do you have a plan on how you will use descriptive statistics in your project?
My project will focus on deprescribing polypharmacy using osteopathy. There will be qualitative data pre-and post-surveys and quantitative numerical data, while quantitative data will need descriptive statistics to detail outcomes. The project will utilize tools to collect quantified data measured as the number of types of or percentage of pills decreased. Various questionnaire tools will also be needed to follow the quality of life markers for sleep, pain, anxiety, and depression. Lastly, descriptive statistics will be required to describe the decrease in medication adequately.
Descriptive statistics is a term used to describe features of a collected data. Descriptive statistics allow one to perform and provide calculation information for quantitative analysis to present more manageable data forms such as a mean, mode, or variance (Salkind et al., 2020). For these reasons and the design of my project, descriptive statistics is going to be the best approach to describe the quantification of the numerical outcomes. What is your plan for the descriptive statistics? Also, do you have a suggestion on a simple way to create a visual descriptive statistics chart?
References
Salkind, N. J., & Frey, B. (2020). Statistics for people who (think they) hate statistics (1st ed.). Sage Publications, Inc.
Week 9 Cowart
Elliott Cowart posted Jun 23, 2021 21:52
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Differentiation of Visual Methods of Displaying Data
Data can be presented in various ways, and this includes displaying it in a visual manner. Salkind (2017) highlights that data is presented with various purposes which affect the manner in which the data is presented, thus the type of chart that is used to present the data (p. 111). Charts provide a form of communication that takes place visually which means that the information that is presented needs to be clear for the reader to discern its meaning. Ensuring that the correct chart is employed to present the data is crucial as this affects the understanding of the data that has been presented while making use of labels allows the audience to further understand the data that is presented to them (Salkind, 2017, p. 101). There are different methods that can be used to create these charts, and this includes computer software applications such as Excel and SPSS.
The different types of charts and graphs that can be used to present data in a visual manner include histograms which indicate the frequency of the occurrence of certain data by means of connected bars. Another visual representation of the data method that can be used is bar charts which are mainly applied to make a comparison between different categories that are presented in a horizontal manner and are placed on the x-axis, while the values of these categories lie on the y-axis. Note that bar charts do not indicate the frequency of data and are therefore different from a histogram in this manner. When the categories are presented horizontally, thus on the y-axis, and the values are on the x-axis, the data is said to be presented on a column chart. This means that a bar chart and column chart can be differentiated based on the placing of the categories and values.
Should the DNP clinician aim to portray the trend with regard to the self-efficacy of the hypertension patients, they can employ a line chart to present their data as this will allow them to highlight the changes that have occurred when the project was initially implemented and after it had been implemented. The line chart is able to indicate the relation that exists between two sets of data (CDC, 2008, p. 1), and in this case, the DNP clinicians can use it to even present the relationship that exists between literacy and hypertension management of the participants. This means that they can successfully determine whether the trend showing improvements or declines in the self-efficacy of participants after partaking in the Intervention. Line charts can show the trend that has been taking place for a specified amount of time, however, one cannot determine the proportion of the improvements or decline of the participants in managing their chronic disease. Pie charts offer DNP clinicians this option, thus being able to indicate the proportion of an item that contributes to the data points that are included in the data (Salkind, 2017, p. 115). Based on this, the DNP clinician can determine the overall percentage of participants that found the implementation of the project effective or show the proportion of participants based on their age and categorize them by use of the internet.
The various methods of presenting data can be confusing and often leads to different mistakes being committed during the process of presenting data visually, and one of these mistakes includes using the wrong type of chart or graph to present data. By making use of an incorrect graph, the information that is displayed automatically changes, thus altering the intended message for the audience (Few, 2004, p. 2). Therefore, it is crucial to avoid using graphs to present data that can be better expressed by means of a table. Titles of the graph are another area that leads to various mistakes being committed such as misleading the audience with regard to the aim of the data, and it is therefore advisable to have a clear and concise title that identifies the purpose of the data that is presented. For graphs that make use of numbers such as age, money, height, etc., it is crucial to ensure that the units of the values that are placed on either the x- or y-axis are provided as this makes it easier for the audience to discern and interpret the data that is presented to them (CDC, 2008, p. 2).
References
Centers for Disease Control and Prevention. (2008). Using Graphs and Charts to Illustrate Quantitative Data. Evaluation Research, 12: 1-2.
Few, S. (September 4, 2004). Common mistakes in data presentation. Perceptual Edge.
Salkind, N. J. (2017). Statistics for people who (think they) hate statistics (6th ed.). London: Sage Publications, Inc.
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