Assignment 2- CHIM 303 S2023

Assignment 2- CHIM 303 S2023. Amuegbunem , Winifred Bagara , Melvie Chukwu, Kelechi Cotoner , Joymercy Espiritu, Lara Fatima Soresho , Shana.

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TABLE OF CONTENTs. 3. CHIM303_Assignment 2- CIHI Case’s mix methodology & Patient Costing.

[Audio] Understanding population health, as facilitated by the POP Grouper and the data sources mentioned in the CIHI grouping population methodology, 1.3, (CIHI, overviews and outputs, 2021) is crucial for predicting health care needs and costs, monitoring population health and diseases, explaining variations in health care resource use, identifying high-cost users, and enabling meaningful comparisons of cohorts. It supports evidence-based decision-making, resource allocation, and targeted interventions to improve population health outcomes. understanding population health through the predictive indicators of health system usage provided by CIHI's POP Grouper allows for effective resource planning, capacity management, preventive interventions, health promotion, disease management, and evidence-based policy development. This knowledge supports the delivery of efficient, targeted, and patient-centered healthcare services, ultimately improving health outcomes and enhancing the overall well-being of the population. Understanding population health using predicted health care cost weights provides valuable information for resource allocation, financial planning, policy development, health system performance evaluation, and promoting equity and accessibility in health care. It enables evidence-based decision-making, cost-effective resource allocation, and the development of targeted interventions to improve the overall health and well-being of the population..

Explain in detail The Use for Case mix methodology.

Explain in detail The Use for Case mix methodology.

[Audio] Allocation of Limited Resources: With a better understanding of population characteristics and individual needs, healthcare providers can allocate their limited resources more effectively. Case-mix methodology enables them to determine the appropriate distribution of resources such as nursing care, therapy times, and physician visits across the healthcare system. By identifying which groups require more resources, providers can ensure that the right level of care is provided to each individual. Decision-Making Support: Case-mix grouping provides valuable information for decision-making in healthcare. By analyzing patterns and trends within specific case-mix groups, providers can make informed decisions about treatment plans, interventions, and resource allocation. The methodology helps identify the most effective and efficient approaches to care based on the clinical characteristics and needs of each group. Quality Improvement and Benchmarking: Case-mix information supports quality improvement efforts in healthcare. By analyzing outcomes and resource utilization within different case-mix groups, providers can identify areas for improvement and implement targeted interventions. The methodology enables benchmarking against similar groups, facilitating the identification of best practices and the development of strategies to enhance the quality of care delivered to specific populations. Program Planning: Case-mix methodology is instrumental in program planning and development. By understanding the characteristics and needs of different populations, healthcare providers can design and implement programs that are tailored to meet those specific needs. The methodology helps in identifying gaps in services and designing interventions that address the unique requirements of each case-mix group. Funding Models: Case-mix methodology is sometimes used as part of funding models in healthcare. By categorizing individuals into different case-mix groups based on their clinical characteristics and resource utilization, funding organizations can determine appropriate reimbursement levels. The methodology ensures that funding is allocated based on the complexity and needs of each group, promoting a fair and efficient reimbursement system..

[Audio] For instance, according to impact on the complexity methodology on St. Michael's Hospital, (FOX et. al, Case Mix Tools, pg. 97), detailed information is presented below regarding the decisions that can be made regarding program planning, funding decisions, staffing, and quality improvement. Program Planning: Analyzing detailed costs assigned to patients provides insights into the actual cost per patient, physician, service, or program. This helps in program planning by identifying areas that may have higher costs than what is credited by the Resource Intensity Weight (RIW) system. The hospital can prioritize resources and allocate funding based on cost-effectiveness and the actual costs of patient care. Funding Decisions: By comparing the actual cost per patient, service, or program with Ministry Program funds or expected costs, the hospital can identify areas that may require additional funding or resources. This analysis can highlight areas with inadequate compensation, such as Cardiovascular Surgery, Haemodialysis, and Trauma. The hospital can use this information to advocate for increased funding or seek reimbursement for services that are not adequately covered. Staffing Decisions: Case mix methodology can help assess the volume of work and patient acuity in different programs and services. This information aids in determining appropriate staffing levels to effectively meet patient needs. Analyzing the total Resource Intensity Weight (RIW) and average RIW per discharge can identify areas with higher patient acuity that may require additional staffing resources. Quality Improvement: Efficiency ratios like length of stay per RIW and total cost per RIW can be monitored to assess patient treatment efficiency and identify areas for quality improvement. Deviations from CIHI percentile benchmarks can highlight areas where improvements are needed. The hospital can focus on enhancing care quality, reducing costs, and maintaining or improving patient outcomes..

[Audio] The Complexity classification is a method developed to enhance the prediction of resource utilization in acute care settings. It builds upon the existing CMG (Case Mix Group) classification and Canadian morbidity coding practices while incorporating clinical judgment and guidelines from the Canadian Institute for Health Information (CIHI). The motivation behind Complexity classification stems from concerns about the accuracy and relevance of case-mix estimates provided by CMG assignments. Some hospitals may believe that certain CMG categories encompass a broader range of patients or treatments that do not align with the specialized care they offer. To address this uncertainty, a more case-specific estimate is introduced. Complexity classification focuses on acute inpatient cases and considers diagnoses beyond the Most Responsible Diagnosis (MRDx) used in CMG assignment. It distinguishes cases with chronic disease conditions unrelated to the primary focus of the acute care episode, cases involving multi-system failure, and cases with complications caused by medical treatment or other factors. The patient's MRDx is still utilized to assign the case to one of the 25 Major Clinical Categories (MCC), and it determines whether the case falls into the medical or surgical partition based on the presence or absence of an operative procedure. However, the step of further assigning cases to specific CMGs based on complications/co-morbidities (CC) or the patient's age range is no longer performed. Instead, Complexity classification addresses these variables in an improved manner. The Complexity Overlay identifies additional diagnoses beyond the MRDx that could reasonably result in a longer hospital stay and more costly treatment. By applying the overlay to the base CMG, cases assigned to the CMG are divided into four new groups or cells for analytical purposes. Age can also be used to further refine the estimation of length of stay and resource use, particularly if it is indicative of these factors. The purpose of age adjustment is not to discourage the documentation of clinical characteristics but to serve as a rough measure for the severity of illness within the CMG and Complexity-defined clinical population. Age and complexity components can be utilized independently or together to predict resource requirements. By implementing the Complexity methodology, healthcare managers can analyze resource needs, track overall resource requirements based on different age proportions, and identify patterns related to demographic or environmental characteristics in acute care treatment. This new approach provides a more comprehensive way of categorizing patients and predicting resource requirements in acute care facilities..

TABLE OF CONTENTs. 10. CHIM303_Assignment 2- CIHI Case’s mix methodology & Patient Costing.

[Audio] The Patient costing video available on CIHI highlights the lack of knowledge regarding the costs of various healthcare procedures and treatments, such as double bypass surgery and treating infections. Patient costing aims to address this knowledge gap and provide accurate information about the costs involved in patient care. Patient costing takes into account various cost factors, including the costs of patient care before, during, and after the procedure. This includes expenses related to medications, imaging, specialized equipment, laboratory tests, administration records, housekeeping, patient meals, and other comforts provided during the hospital stay. By implementing patient costing, healthcare leaders and providers can make informed decisions about patient care and the healthcare system. It provides a guide for analysis, comparison, and evaluation of approaches and procedures across different regions and healthcare facilities. Patient costing enables the identification of best practices, facilitates transparency and accountability, and helps estimate spending, ultimately supporting the efficient allocation of resources and the sustainability of healthcare services. In summary, patient costing plays a crucial role in providing accurate information for decision-making, promoting accountability, estimating spending, and identifying efficient ways of delivering healthcare services. It is a step towards meeting the challenges faced in healthcare and enables healthcare leaders, providers, and citizens to make better-informed decisions. Top of Form.

[Audio] Mäenpää et. al write that privacy concerns with digitizing medical records allowed for data standards being implemented on a political level. (Mäenpää et. al , 2009) EMRs are important to both patient and healthcare provider, and it comes with safeguards against invasion of privacy..

[Audio] CPOEs and the benefits that it can provide would be irrelevant if it is barely implemented in the areas that need them. Continuing with the Anderson and Aydin article, they mention that CPOE was not available to physicians in 84% of hospitals. (Anderson and Aydin, 2005). At the time of the article's writing, most of the hospitals are still analog. Phasing out analog faculties to usher in technology creates unease in the workplace. Workers and patients alike find this transition difficult since it disrupts already set work habits and culture for the worker and patients still prefer human-to-human contact in care. Digitizing the workplace was a "new" way of innovating data transfer. The transition is obviously a challenge since it requires numerous moving parts coming together. Health care providers have concerns about how the EMR can complicate the protection of patient's privacy and their experience makes them not fully trust the system. "The need for confidentiality is a response to privacy concerns that are also very important in the health care sector due to the very sensitive data regarding patients and clients that they carry." (Keshta, Odeh, 2021 pg. 2). The electronic medical record can pose challenges to patients' confidentiality. The network could be vulnerable due to a data breach or high-tech cyber attack and the patient's medical data could be at risk. Another concern include the loss of information due to a natural disaster..

references. Anderson, J. G., & Aydin, C. E. (2005). Overview: Theoretical perspectives and methodologies for the evaluation of healthcare information systems. Evaluating the organizational impact of healthcare information systems , 5-29. Keshta , I, Odeh, A. (2021). Security and privacy of electronic health records: Concerns and challenges. Retrieved from https:// reader.elsevier.com /reader/ sd / pii /S1110866520301365?token=C748EBF3F82DE1109174C86AEAAC64FACC21C831A5223D5422313489EC2256451EE9E1B2C778AEE56DCF124024BF3CE8&originRegion=us-east-1&originCreation=20211121162049 Mäenpää , T., Suominen , T., Asikainen , P., Maass , M., & Rostila , I. (2009). The outcomes of regional healthcare information systems in health care: a review of the research literature. International journal of medical informatics , 78 (11), 757-771..