5
MIROCHNIK-
Introduction
Quality improvement in all healthcare settings is core in addressing re-hospitalization issues, especially amongst diabetic patients and substance abuse patients due to treatment non-compliance. Markedly, each health care system is established on a complex network of care pathways and processes. The care quality delivered by the system depends to a greater extent on how efficient this network operates and how best the individuals who manage and provide care work together. Quality improvement aims to provide high standard quality care to patients and elevate a nation’s population health. Quality improvement entails giving the people closest to the problems and issues affecting the care with eminence skills, time, permission, and assets they may be needing to find the solution (Kumar 2017). Quality of data influences the usability of the data in HER (Health Electronic Records) systems. Generally, the data should always be relevant and accurate, and those who collect data must validate its quality. It is due to accuracy is one of the critical aspects of blockchain interoperability. Therefore, the Interoperable electronic health record (EHR) is the most suitable application that uses data to confirm care plans for diabetic patients and for substance abuse patients re-hospitalized due to treatment non-compliance.
Quality Improvement Method
The Interoperable electronic health record is the quality improvement methodology of choice for this project, mainly because it gives room for sharing patient’s information electronically. Primarily between healthcare providers and various EHR systems, improving and enhancing the ease of providing healthcare by practitioners to diabetic patients and for substance abuse patients re-hospitalized due to treatment non-compliance, explicitly ensuring that these patients freely move in various care facilities. (Kumar 2017. Interoperability influence most related health information technology, through the four major domain functionalities in the EHR systems that assist the clinicians in decision making, for example, patient demographic data records and clinical data, managing and viewing of laboratory tests results and data imaging, management of electronic prescription and order entry and lastly supporting clinical decisions that includes drug contraindications warnings and drug interaction of the hospitalized diabetic and substance abusers. Worthy mentioning, EHR is the quality improvement methodology of choice majorly because the methodology applicability through the healthcare systems allows the use of specified EHR systems based on different factors, such as state requirements and care facility requirements.
This model allows the patient to enter any clinic or hospital that shares the same type of machine learning system and can still access patient information. Also, having data within a single point access will allow all data to be collected in a single patient to develop the best care plan. This was made possible under the Health Information Technology for Economic and Clinical Health (HITECH), a 2009 act. The EHR systems have made much progress, particularly by promoting health details technology meaningful use (Varkley 2016). Significantly, EHR systems are the best quality methodology since it allows for elation whereby the patient’s details and the story is put at their care experiences’ center, regardless of whether the story includes theirs out of or in physicians’ network visits. For example, under the EHR systems, the Elation Collaborative Health Record (CHR) focuses on cross-communication facilitation, especially amongst providers. CHR is designed as a dashboard centralized with patients’ test results, notes, and stories and can be managed by every physician treating the patients. This approach addresses documents being faxed, increasing the likelihood of compliance and patient privacy issues.
Tools to be used in Identifying Current and Future Workflow State
Before creating an application that will use data to form care plans for diabetic patients and substance abuse patients re-hospitalized due to treatment non-compliance, it is prudent to understand the application of the quality improvement tools. (Silver 2015). This is achieved by sparing analyzing various data in partnership with patients and staff, which will aid in justifying the various quality improvement tools. For instance, in blockchain interoperability, data inconsistency of patients from the facilities was witnessed; thus, it is critical to understand the root cause of the same for a suitable quality improvement plan. (Silver 2015). This issue will be improved by applying a cause and effect tool in problem identification that is always designed to enable players to identify nearly all the potential causes, not just the most obvious ones. Immediately after placing the application default such as data inconsistency, members can now employ various tools for further investigation, such as process mapping, survey, and patient interviews.
Process Mapping Tool
The process mapping tool is used to verify every process investigation step. It is used to map the journey or pathway in part or all the patient supporting processes and health care journey. Patients’ journey mapping is essential for patients who are re-hospitalized less than 30 days for diabetes, mental health, and substance abuse patients, which involve multiple healthcare providers, is critical to the identity of any quality challenges which occur at the interludes between organizations and teams.
A driver diagram and logical model tools
It is essential to give a convenient time when designing an improvement intervention and the delivery period. The development of interoperability blockchain applications requires specific objectives and measurable and clear targets. This could aid in resources and support attraction from managers and leaders to smooth implant interoperability applications to assist the patients. It is also essential to identify any subsequent challenges to be addressed to achieve the application’s efficiency. A driver diagram captures the significant issues and identifies plans required to tackle them Glouberman 2018). If specifying an intervention, it is essential to review how other applications had been improved. This helps to reduce the risk of reinventing the wheel by repeating a similar work that could have already been done somewhere. (Glourberman 2018).
Lean and Six Sigma Tools
Lean and Six Sigma refers to the culture and practice of relentless waste elimination to ensure that every service provided is of high quality, available and safe at the required time, and delivered at a friendly cost. The current and the future workflow state will be identified by both the lean and six sigma quality improvements methods concerning the project application of interoperability. This will be achieved by applying the following tools A3 Thinking, PDSA Cycles, and DMAIC (Celenza 2017 The A3 thinking method enables the interoperability application to achieve a sensible, concise overview of the future and current operations and foretell precisely the potential bottlenecks in the interoperability project application (Bennedetto 2016).
Impact of Quality Improvement Methodology or Tools on the Project’s Scope or the defined business problem
Data and measurement improvement
Gathering and measurement of data are crucial elements to elevate quality or performance and are required to assess the set aims against the impact. In this project, when attempting to evaluate the effect of a change to a more complex system using the blockchain interoperability, integration of measures was improved mainly through outcome and process measures (Benedetto 2016). It is also essential to measure the application’s state without the intervention, considering the potential external causes of change witnessed in the measurement. This was achieved using the static process control (SPC), a method under the blockchain interoperability systems that explores the difference between common variations which can be regulated. (Bennedetto 2016).
Data consistency
Data inconsistency had been an issue that led to massive re-hospitalization of diabetic patients and substance abuse patients following treatment non-compliance. This is true since using blockchain interoperability system data inconsistency of patients from the various facilities was witnessed (Arefah 2018). As a result, it is essential to understand the root cause of the same for a suitable quality improvement plan, which is an issue that was improved through the application of cause and effect tool in problem identification that was designed to enable players to identify nearly all the potential causes not just the most obvious ones (Arefah 2018). Following identifying the cause of inconsistency, healthcare practitioners will employ various tools for further investigation, such as process mapping, surveys, and patient interviews.
Conclusion
Quality improvement in all healthcare settings is core in addressing re-hospitalization issues, especially amongst diabetic patients and substance abuse patients due to treatment non-compliance. In this project, the Interoperable electronic health record (EHR) is the most suitable application that uses data for confirming care plans for diabetic patients and for substance abuse patients who are re-hospitalized due to treatment non-compliance. Sigma and lean six and EHR systems were used since many professionals in healthcare use these quality improvement tools. The EHR systems were selected as the universal quality improvement tool in assessing the interoperability blockchain application since its principles are similar to the Joint Commission principles. These principles focus on the importance of the need for excellent healthcare leadership and stakeholder’s interests. The tool approach focuses on objective decision-making standards that would assist the clinicians to make informed decisions when handling the re-hospitalized diabetic, substance abusers, and mental health patients and developing care plans. Implementation of quality standard data measures as exposed by the tool would also assist the clinician in making scientifically informed decisions when providing the best quality of care for the patients.
References
Arafeh, Mazen, Mahmoud A. Barghash, Nirmin Haddad, Nadeem Musharbash, Dana Nashawati, Adnan Al-Bashir, and Fatina Assaf. 2018. “Using Six Sigma DMAIC Methodology and Discrete Event Simulation to Reduce Patient Discharge Time in King Hussein Cancer Center.” Journal of Healthcare Engineering 2018: 1–18. Compass.
Celenza JF;Zayack D;Buus-Frank ME;Horbar. 2017. “Family Involvement in Quality Improvement: From Bedside Advocate to System Advisor.” Clinics in Perinatology. U.S. National Library of Medicine. September 4. https://pubmed.ncbi.nlm.nih.gov/28802339/.
Kumar, U Dinesh, John Crocker, T. Chitra, and Haritha Saranga. 2020. “Reliability and Six Sigma: U Dinesh Kumar.” Springer. Springer US. March 12. https://www.springer.com/gp/book/9780387302553.
Silver, Samuel A., Mitra K. Nadim, Donal J. O’Donoghue, Francis P. Wilson, John A. Kellum, Ravindra L. Mehta, Claudio Ronco, et al. 2020. “Community Health Care Quality Standards to Prevent Acute Kidney Injury and Its Consequences.” The American Journal of Medicine 133, no 5: 230-242.Compass.
Laverentz, Delois Meyer, and Sharon Kumm. 2017. “Concept Evaluation Using the PDSA Cycle for Continuous Quality Improvement.” Nursing Education Perspectives 38 , 5: 288–90. Compass.
The post 5 MIROCHNIK- Introduction Quality improvement in all healthcare settings is core in appeared first on PapersSpot.
