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Discussion: Identifying Practice Gaps for Quality Improvement

Discussion: Identifying Practice Gaps for Quality Improvement

NURS 8302 Discussion: Identifying Practice Gaps for Quality Improvement

Every healthcare organization is expected to deliver quality services to its clients. It, therefore, remains the role of every healthcare provider to ensure their clients have access to quality. In cases of a gap in quality, a lot of input is required to close the gap to achieve the highest level of quality services that the organization requires. A critical component of improving quality scores and outcomes is recognizing the challenges faced by healthcare providers in closing gap in care and and then building quality improvement strategies.  My discussion below analyzes the steps towards achieving a close to perfect delivery of healthcare that would benefit my organization as well.

The perfect way to identify a gap in the quality of the healthcare delivery system is through the claims data. Claims data is an element of data that contains a preview of quality from both the clients and the providers. An analysis of the claims data is a perfect reflection of what goes on within the healthcare fraternity that pertains to the healthcare gaps within each situation (Riley et al., 2020). In the current organizations, providers are not equipped to meet the main drivers of quality. However, it is possible to trace the problem and define a perfect solution through the claims data.

A potential quality improvement program is realized through provisions of timely and quality action scorecards. The design of the scorecards helps deliver the message of improvement to the healthcare providers, including the nurses. Timely and quality action scorecards given to providers allow them to realize the gap in providing care to their patients, and so they begin to implement models of satisfying their patient’s needs (Sulo et al., 2017).

The first tool that can be used to address the quality improvement practice gap is population health analytics that assesses the population health risk and stratifies the solutions based on the gaps and risks identified within a  population (Tappen et al., 2017). For example, in a unit with a quality complaint through health analytics, it is possible to deploy more providers in the unit. The other tool is face-to-face contact, where the expert engages others until an agreement is reached on the solution to quality improvement and related gaps.

References

Riley, K., Sulo, S., Dabbous, F., Partridge, J., Kozmic, S., Landow, W., … & Sriram, K. (2020). Reducing hospitalizations and costs: a home health nutrition‐focused quality improvement program. Journal of Parenteral and Enteral Nutrition44(1), 58-68. https://aspenjournals.onlinelibrary.wiley.com/doi/full/10.1002/jpen.1606.

Sulo, S., Feldstein, J., Partridge, J., Schwander, B., Sriram, K., & Summerfelt, W. T. (2017). Budget impact of a comprehensive nutrition-focused quality improvement program for malnourished hospitalized patients. American health & drug benefits10(5), 262. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5620506/.

Tappen, R. M., Wolf, D. G., Rahemi, Z., Engstrom, G., Rojido, C., Shutes, J. M., & Ouslander, J. G. (2017). Barriers and facilitators to implementing a change initiative in long-term care utilizing the INTERACT™ quality improvement program. The health care manager36(3), 219. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5533173/.

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Post a brief explanation of how you would identify a quality improvement practice gap in your practice or organization.

Quality improvement projects require input from a multitude of sources to encourage appropriate, safe and cost-efficient patient care and outcomes.  Healthcare systems that operate in a culture where quality improvement is included in strategic plans and continuously evolve to meet the emergent care needs of patients and team members demonstrate higher patient outcomes (Mannion & Davies, 2018).  Understanding that a cyclic approach to developing quality improvement projects driven by data should align with organizational goals (Agency for Healthcare Research and Quality 2018).  Data drives the construction and design of quality improvement projects as it shows developers where potential areas of improvement reside by comparing standards of practice to evidence based benchmarks (Steele et al., 2012).  Sattler et al., (2019) demonstrated through plan-do-study-act cycle in the Interventional Radiology (IR) Unit focused on throughput times how a team approach to quality improvement positively influenced changes.  Through communication, delegation and interdisciplinary team approach, Sattler et al., (2019) showed where delays for cases occurred throughout the intake process.  Unit wide ownership of quality improvement project by stakeholders, who included nurses, physicians, advanced practice nurses and IR technologists, were effective in decreasing throughput times which translated into faster start times and improvements in patient satisfaction.

Bedside nurses appreciation of what quality improvement means for patient care is the initial point of developing a practice gap project.  Nursing quality improvement projects in the acute care setting begins with education in nursing schools on what quality means to patient outcomes and healthcare fiscal responsibilities.  Hassmiller & Bolton (2009) explain that the future of nursing demands that students learn their roles in quality improvement projects, how to identify potential improvement projects and what these projects mean to the care continuum.  The authors also highlight the importance of healthcare organizations aligning their strategic plans with quality improvement in mind to ensure the entire healthcare system recognizes what quality means for patient outcomes.

Describe a potential quality improvement practice gap you might use for your DNP project, and explain why.

Identification of an appropriate quality improvement practice gap in the Neuro-Interventional Radiology Unit (Neuro-IR) starts by assessing benchmarks generated by data regarding first case start times (Steele et al., 2012).  The goal of this project is to standardize care to minimalize delays in first case start times through use of interdisciplinary developed checklists.  Using delay codes in the documentation record of the Neuro-IR setting will identify trends in first case start delays (Moeslein & Nagy, 2011).  Examination of trends illuminates each stakeholder’s responsibility for case delays and how a quality improvement project will work to their specific discipline’s benefit to improve start times.  Assessment of first case start times in the Neuro-Interventional Radiology (Neuro-IR) provides insight into care and cost efficiency that influence patient outcomes and care costs (Moeslein & Nagy, 2011).  The workflow within the Neuro-IR department effects the unit’s ability to generate revenue as both and inpatient and outpatient setting.  Overtime costs and staffing issues are negatively associated with delays in case start times as well as patient satisfaction which all influence the financial status of the unit and healthcare setting (White, 2018).  Research presented by White (2018) used preprocedural checklists as an intervention that were tested through a series of plan-do-study-act cycles to determine what items were necessary for the quickest yet thorough assessment in the pre-procedure workup.  Retrospective chart analysis pre and post intervention showed that email reminders to the care team, accountability to leadership and unit facilitators helped improve safe patient care transitions that equated to enhanced first case start times.

Then, explain at least two types of tools and/or methods you might use to address this quality improvement practice gap, and explain why. Be specific and provide examples.

Selecting an appropriate quality improvement method to identify, assess and treat a practice gap in the Neuro-IR is an arbitrary choice that should incorporate the input of stakeholders.  One practice team or discipline should not be responsible for all aspects of quality improvement project that effects the of patients in that designated area.  Two methods used to address quality improvement practice gaps associated with first case start times are the lean and TCAB project methods.  The TCAB methods, also known as “transforming care at the bedside,” is a nurse driven initiative that focuses on workflows to ensure nurses optimize their time at the bedside.  When applied to the Neuro-IR, a potential TCAB project would start by evaluating how much time nurses spend performing perioperative assessments and how that time influences potential delays on cases starting the in the procedural area.  Application of an assessment script to the nurses would be the testing agent.  Pre and post intervention evaluations of time would be distributed to care teams.  The use of rapid-cycle testing, as presented by Hassmiller & Bolton (2009) allows members of the quality improvement team to actively, rather than passively, assess the success of the intervention while it is in use. In this case, questions in the perioperative assessment would be tailored to shorten the time spent completing the assessment.  White (2018) discusses the use of checklists in the perioperative setting that direct care to minimize care delays Use of the TCAB method to address the quality improvement gap associated with first case start times is nurse driven primarily however input from other care services of the Neuro-IR suite, such as physician assistants and anesthesiologists, is imperative for system improvements.  TCAB quality improvement projects accentuate the areas in care where gaps exist and identifies areas where waste, either literal or figurative such as time, can be better managed (Hassmiller & Bolton 2009).

The second tool that can be applied to facilitating efficiency in first case start times is the “lean” model of quality improvement.  This model also addresses inefficiency in practice by assessing wasteful processes through a team approach (Isaacson et al., 2014).  The lean model is data driven and uses communication tools, such as signs, emails and check sheets, to mitigate time consuming activities that impede first case start times.  Lean uses the processes of define, measure, analyze, improve and control to initiate, test and maintained quality improvement strategies that translate into practice changes (Dowell et al., 2017).  This is a less nursing focused venture but still uses data to drive improvements in outcomes.

Reference

Agency for Healthcare Research and Quality.  (2018).  Key driver two: implement a data driven quality  improvement process to integrate evidence into practice procedures.  Retrieved from:  https://www.ahrq.gov/evidencenow/tools/keydrivers/implement-qi.html.

Dowell, J. D., Makary, M. S., Brocone, M., Sarbinoff, J. G., Vargas, I. G., & Gadkari, M. (2017). Lean Six Sigma Approach to Improving Interventional Radiology Scheduling. Journal of the American College of Radiology, 14(10), 1316–1321. https://doi.org/10.1016/j.jacr.2017.02.017

Isaacson, A., Ridge, N., Yu, H., & Jackson, M. (2014). “Lean” System Improvement to Increase First-Case Efficiency and Decrease Overtime Expenditures in Interventional Radiology. Journal of the American College of Radiology, 11(10), 998–1001. https://doi.org/10.1016/j.jacr.2013.12.003

Hassmiller, S. B., & Bolton, L. B. (2009). The Development of TCAB. AJN, American Journal of Nursing, 109(11), 4. https://doi.org/10.1097/01.naj.0000362009.68589.f1=

Mannion, R., & Davies, H. (2018). Understanding organisational culture for healthcare quality improvement. BMJ, 363(363), k4907. https://doi.org/10.1136/bmj.k4907

Moeslein, F., & Nagy, P. (2011). Performance Quality Improvement Projects: Suggestions for Interventional Radiologists. Journal of the American College of Radiology, 8(8), 585–587. https://doi.org/10.1016/j.jacr.2011.04.014

Sattler, M., Morrison, T., Powell, T., & Steele, D. (2019). Improving Throughput in Interventional Radiology: A Team Collaboration. Journal of Radiology Nursing, 38(3), 188–192. https://doi.org/10.1016/j.jradnu.2019.06.001

White, V. A. (2018). Improving Workflow Efficiency in Interventional Radiology. Journal of Radiology Nursing, 37(3), 202–204. https://doi.org/10.1016/j.jradnu.2018.06.004

The methodology I would utilize to identify a quality improvement gap in my current organization is the hospitals’ scorecard on Hospital Compare.  “The Centers for Medicare & Medicaid Services (CMS), the federal agency that runs the Medicare program, created this tool in collaboration with organizations representing people with Medicare, hospice organizations, other stakeholders, and other federal agencies”. (Center for Medicare and Medicaid Services, n. d.).  CMS’s website, hospital compare, is a website that allows participants/caregivers/others, to identify providers based on the criteria most relevant to them.  In reviewing my current organizations’ scorecard via hospital compare, the hospitals’ rate for 30- day inpatient psychiatric readmissions is 21.1%, while the national average is 20.1, which is not statistically significant.  However, the percentage of patients included in this data (Medicare only), are minimal, compared to the total number of patients discharged from the inpatient psychiatric unit.

To be sure that this data was accurate, I compared this data to the same statistics reported by some of our other payors via the Value-Based Purchasing (VBP) program. This is a method of provider payments, directly related to the providers’ performance.  More specifically, providers are held accountable for the quality of care they provide, which is directly related to the organizations’ reimbursement (HealthCare.gov, n. d.).  My organization currently has VBP agreements with two payors.  As reported by these payors, our 30-day readmission rate for the inpatient psych unit is significantly higher than the local/state/national average.  Thus, this is an opportunity for our department to address.

The method I would choose to address this gap in practice is a retrospective chart review. This is a review of a medical record after a patient has been discharged from the hospital.  It is the most comprehensive method of data collection related to patient care.  Additionally, a retrospective chart review “is the primary tool for answering the “why” of a given situation” (Nash, Joshi, Ransom, E. & Ransom, S., 2019, pg. 114).  Following a patients’ discharge, a thorough review of a patients’ chart could help to provide some detail regarding the patients’ discharge plan, wrap-around services in the community and other factors, or lack thereof, that may have contributed to the patients’ readmission.

After a thorough review of a sampling of patients’ charts, collecting data, I would use a fishbone diagram to assist in sorting the data, identifying potential causes for patients’ readmissions.  The fishbone diagram organizes possible causes, in a format that’s visual for ease in understanding (Nash et al., 2019).  Although the reasons for readmission may be very individualized, my goal would be to ensure that, as an organization, we provided patients with all of the tools required to be successfully discharged to the community. Overall, in a review of this data, the focus would be on psychiatric readmissions, but would also consider patient outcomes.  “Management strategies to reduce readmissions may influence indicators of well-being such as psychiatric symptomatology, functional status, quality of life, social adjustment, self-efficacy, service satisfaction, life skills, medication adherence, and ability to live independently” (RTI-UNC Evidence-based Practice Center, 2015, pg. 25).  Improved quality of life is a goal for patients with severe and persistent mental illness.

References

Center for Medicare and Medicaid Services (n. d.).  Hospital Compare. https://www.medicare.gov/care-compare/resources/about-this-tool

HealthCare.gov (n. d.).  Value-Based Purchasing.  https://www.healthcare.gov/glossary/value-based-purchasing-vbp/

Nash, D. B., Joshi, M. S., Ransom, E. R., & Ransom, S. B. (Eds.), (2019).  The healthcare quality book: Vision, strategy, and tools (4th ed.). Health Administration Press.

RTI-UNC Evidence-based Practice Center (2015). Effective health care program: Management strategies to reduce psychiatric readmissions. Technical Brief Number 21. https://effectivehealthcare.ahrq.gov/sites/default/files/pdf/psychiatric-readmissions_technical-brief.pdf

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Response

This is an outstanding work Andretta. The methodologies of identifying quality improvement gap in health care organization have been clearly identified. I concur with your post. Ideally, identification of quality improvement gap should be followed by addressing the gap and determination of quality of the service. However, evaluating the perceptions and expectations of patients concerning health care delivery and areas that need improvement remains a great challenge (Javed & Ilyas, 2018). Usually, surveys have been used to understand quality improvement gaps from the perspective of the patients and other stakeholders. However, another important methodology that organizations and providers can use is to compare the perceived and the expected quality of service and identify the gaps between them, utilize the feedback from the stakeholders to enhance quality, and evaluate the real experiences of health care (Lu et al., 2020). As service quality is increasingly becoming a critical aspect of corporate strategy in health care system, an approach of measuring the perception and understanding the gap between perception and expectation can be important in quality improvement initiatives (Dopeykar et al., 2018).

References

Dopeykar, N., Bahadori, M., Mehdizadeh, P., Ravangard, R., Salesi, M., & Hosseini, S. M. (2018). Assessing the quality of dental services using SERVQUAL model. Dental Research Journal, 15(6), 430. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6243813/

Javed, S. A., & Ilyas, F. (2018). Service quality and satisfaction in healthcare sector of Pakistan—the patients’ expectations. International journal of health care quality assurance. https://doi.org/10.1108/IJHCQA-08-2016-0110

Lu, S. J., Kao, H. O., Chang, B. L., Gong, S. I., Liu, S. M., Ku, S. C., & Jerng, J. S. (2020). Identification of quality gaps in healthcare services using the SERVQUAL instrument and importance-performance analysis in medical intensive care: a prospective study at a medical center in Taiwan. BMC Health Services Research, 20(1), 1-11. https://doi.org/10.1186/s12913-020-05764-8

 

As mentioned in the introduction to this week’s topic, a practice gap is the difference between a desirable or achievable state of practice and current reality. One strategy to identify a quality improvement practice (QI) gap is to conduct a gap analysis. A gap analysis is used to identify gaps in services or processes and helps clarify the differences between reality and the perceptions of practices in the organization (American Medical Writers Association (AMWA), 2021). Identifying practice gaps can also help to better focus resources and energy on those identified areas in order to improve them (AMWA, 2021). Lu et al. (2020), suggest using SERVQUAL, to compare the expected and perceived service quality to identify gaps between them, apply feedback from patients to improve quality of care, assess experiences of medical care, and perceptions of quality as provided by the patients.

A potential quality improvement practice gap is the lack of medication adherence. The lack of medication adherence has long affected patients, healthcare outcomes, and the overall healthcare system. Some factors that influence medication nonadherence are inadequate understanding of medications,side effects, and the inability to afford the medications. Whatever the details are surrounding medication nonadherence, it remains as a gap in healthcare practice. Medication nonadherence directly affects 30-day hospital readmission and the increasing healthcare costs. Medication nonadherence has even extended globally with an estimated $289 billion in damages (Lloyd et al., 2019). One does not have to look far when trying to locate issues with medication nonadherence. On the medical-surgical telemetry floor where I work, we tend to see the same heart failure (HF) patients almost every month or every couple of months due to HF exacerbation. Retrum et al. (2013) states that HF is the leading cause of hospital readmission and hospitalization in older adults.

The Agency for Healthcare Research and Quality (2018), suggests using a systemic approach to address quality improvement gaps by adopting a consistent QI approach like model for improvement, lean, six sigma, root cause analysis, and plan-do-study-act (PDSA). To address the medication nonadherence gap, the PDSA tool could be implemented to determine the nature and scope of the problem, what changes can and should be made, a plan for a specific change, who should be involved, what should be measured to understand the impact of change, and where the strategy will be targeted. Change is then implemented and data and information are collected. Results from the implementation study are assessed and interpreted by reviewing several key measurements that indicate success or failure.

Another tool that can be used to address medication nonadherence is the conduction of a root cause analysis (RCA). RCA is a technique used to identify trends and assess risk that can be used whenever human error is suspected with the understanding that system, rather than individual factors, are likely the root cause of most problems. Medication adherence is a complex behavior that is influenced by factors along the continuum of care, relating to the patient, providers, and health systems. Patient-related factors include unintentional factors, which often worsen with increasingly complex medication regimens (e.g., forgetting to take medication or obtain refills, or inadequate understanding of dose or schedules); and intentional factors (e.g., active decision to stop or modify a treatment regimen based on ability to pay, beliefs and attitudes about their disease, and medication side effects). Conducting a root cause analysis can specify the cause or causes and hopefully address the issue on the system level.

References

Agency for Healthcare Research and Quality. (2018). Key Driver 2. Implement a data-driven quality improvement process to integrate evidence into practice procedures. https://www.ahrq.gov/evidencenow/tools/keydrivers/implement-qi.htmlLinks to an external site.

Lu, S. J., Kao, H. O., Chang, B. L., Gong, S. I., Liu, S. M., Ku, S. C., & Jerng, J. S. (2020). Identification of quality gaps in healthcare services using the SERVQUAL instrument and importance-performance analysis in medical intensive care: A prospective study at a medical center in Taiwan. BMC Health Services Research, 20(1), 908. https://doi.org/10.1186/s12913-020-05764-8Links to an external site.

Lloyd, J. T., Maresh, S., Powers, C. A., Shrank, W. H., & Alley, D. E. (2019). How much does medication nonadherence cost the medicare fee-for-service program? Medical Care, 57(3), 218-224. https://doi.org/10.1097/MLR.0000000000001067 Links to an external site.

Retrum, J. H., Boggs, J., Hersh, A., Wright, L., Main, D. S., Magid, D. J., & Allen, L. A. (2013). Patient-identified factors related to heart failure readmissions. Circulation. Cardiovascular Quality and Outcomes6(2), 171–177. https://doi.org/10.1161/CIRCOUTCOMES.112.967356Links to an external site.

Great job Quennie! I appreciate your insightful post addressing the practice gap and the challenges associated with new nurses, staffing issues, and the overall quality of care. Your emphasis on process-oriented thinking, quality improvement strategies, and the use of run charts in addressing staffing concerns is well-founded. Given the specific context of inconsistent adherence to diabetes management guidelines, some additional tools and methods might be considered to address the quality improvement practice gap. For example, conducting a thorough RCA can help identify the underlying causes of inconsistent adherence to diabetes management guidelines (Widyaputri et al., 2022). This method involves systematically analyzing events to discover the root causes, allowing for targeted interventions.

Next, developing and implementing clinical pathways for diabetes management can provide a standardized approach to care. Introducing checklists and decision support tools within the electronic health record (EHR) system can serve as practical aids for nurses (Chamba et al., 2022). These tools can help guide them through essential steps in diabetes management, ensuring key aspects are not overlooked. Implementing simulation training for nurses can be beneficial in bridging the theory-practice gap. Simulated scenarios related to diabetes management can help build confidence, improve critical thinking skills, and enhance adherence to guidelines in real-life situations.

Again, establishing a peer mentoring program pairs experienced nurses with newer ones to provide guidance, share insights, and facilitate knowledge transfer (Macneill et al., 2018). Encouraging open communication and shared decision-making can enhance the overall quality of care.  Providing clear and accessible educational materials, support groups, and digital resources can improve patient adherence to self-management guidelines. Implement regular audits of diabetes care processes and provide constructive feedback to nursing staff. This continuous monitoring can identify areas for improvement and motivate adherence to guidelines.

 

References

Nyasatu G. Chamba, Kenneth C. Byashalira, Dirk L. Christensen, Kaushik L. Ramaiya, Eliakimu P. Kapyolo, PendoMartha J. Shayo, Troels Lillebaek, Nyanda E. Ntinginya, Blandina T. Mmbaga, Ib C. Bygbjerg, Stellah G. Mpagama, & Rachel N. Manongi. (2022). Experiences and perceptions of participants on the pathway towards clinical management of dual tuberculosis and diabetes mellitus in Tanzania. Global Health Action, 15(1). https://doi.org/10.1080/16549716.2022.2143044

Macneill, G., Christo, C., Gorecki, K., & Tirel, C. (2018). 66 – Gestational Diabetes Management Mentoring Intervention. Canadian Journal of Diabetes, 42(5), S26. https://doi.org/10.1016/j.jcjd.2018.08.071

Widyaputri, F., Rogers, S. L., Khong, E. W. C., Nankervis, A. J., Conn, J. J., Sasongko, M. B., Shub, A., Fagan, X. J., Guest, D., Symons, R. C. A., & Lim, L. L. (2022). Prevalence of diabetic retinopathy in women with pregestational diabetes during pregnancy and the postpartum. Clinical & Experimental Ophthalmology, 50(7), 757–767. https://doi.org/10.1111/ceo.14111