DISCUSSION REPLIES
2 references each response
2 years ago
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Transformingweek5replies.docx
Leadingweek5replies.docx
Transformingweek5replies.docx
Transforming week 5 replies
Read the four students post and Expand upon your colleague’s posting or offer an alternative perspective. Include 2 references each
MOM-STELLA:
Predictive analytics, facilitated by Big Data, Data Science, Data Mining, Data Analytics, and Machine Learning, holds immense promise for revolutionizing healthcare delivery. In nursing practice, one practical application of these technologies involves leveraging predictive analytics to forecast and prevent pressure ulcers. By managing vast amounts of patient data, including mobility patterns, nutritional status, comorbidities, and skin assessments, predictive analytics can identify individuals at high risk of developing pressure ulcers. Nurses can then employ targeted interventions, such as frequent repositioning, specialized support surfaces, and personalized skin care regimens, to mitigate the risk of pressure ulcer formation in these vulnerable patients (Getie et al., 2020; Liu et al., 2024).
Despite the potential benefits, integrating predictive analytics into healthcare encounters challenges and opportunities. A primary challenge is establishing robust data collection and integration systems capable of aggregating and harmonizing data from diverse sources, including electronic health records, medical devices, and wearable sensors. Additionally, concerns regarding data privacy, security, and ethical considerations may arise, particularly regarding the utilization of sensitive patient information for predictive modeling (Guetterman, 2019).
Nevertheless, predictive analytics offers numerous opportunities for improving patient outcomes, optimizing resource allocation, and advancing healthcare delivery. By accurately predicting adverse events or disease progression, healthcare providers can intervene preemptively to prevent complications and enhance patient safety. Furthermore, predictive analytics can facilitate personalized medicine by tailoring treatment plans to individual patient characteristics and predicting response to therapy (Walther et al., 2022).
Looking forward, advancements in artificial intelligence, machine learning, and big data analytics are poised to further enhance the capabilities of predictive analytics in healthcare. This includes the development of more sophisticated algorithms for predictive modeling, the integration of real-time data streams for dynamic risk assessment, and the utilization of predictive analytics to inform clinical decision-making at the point of care. Nonetheless, addressing challenges related to data quality, interoperability, and algorithm transparency will be critical to ensure the responsible and effective deployment of predictive analytics in nursing practice and broader healthcare contexts.
MOM-JENNIFER:
However, challenges exist in the implementation of predictive analytics in healthcare. One significant challenge is ensuring the accuracy and reliability of predictive models, as they rely heavily on the quality and completeness of the data used for analysis. Additionally, there may be concerns regarding patient privacy and data security, particularly when utilizing sensitive health information for predictive purposes (Keim-Malpass et al., 2020). Furthermore, healthcare organizations may face barriers in integrating predictive analytics into existing workflows and clinical decision-making processes, requiring investment in training and infrastructure to leverage these technologies effectively.
Despite these challenges, the future of predictive analytics in healthcare holds promising opportunities for improving patient outcomes and driving efficiencies in healthcare delivery. As predictive models become more sophisticated and refined through advancements in machine learning and artificial intelligence, they have the potential to revolutionize preventive care by enabling proactive interventions tailored to individual patient needs (Keim-Malpass et al., 2020). By harnessing the power of data-driven insights, nurses can play a pivotal role in promoting patient safety and quality of care, ultimately shaping the future of nursing practice towards a more proactive and personalized approach to healthcare delivery.
Although they are widespread in healthcare, predictions of this kind present yet another set of opportunities for improving patient outcomes and driving efficiency in healthcare delivery. The more sophisticated and refined predictive models are, through developments in machine learning and artificial intelligence, they have the potential to revolutionize preventive care by enabling proactive interventions tailored to individual patient needs (Battineni et al., 2020). With the power of data-driven insights, nurses can be instrumental in ensuring the patient's safety and quality of care and, hence, make their future nursing practice all the more proactive and personalized when it comes to healthcare delivery.
ANGELA:
Describe a Practical Application for Predictive Analytics in Nursing Practice
Data and predictive analytics (PAs) in various forms are critical to advancements in healthcare (Marino et al., 2020). Data processing enables stakeholders to enhance health care for disease processes, improve risk prediction, and elevate diagnostic accuracy (Marino et al., 2020). This discussion aims to explain how PAs are used in nursing practice. Predictive analytics also allows healthcare professionals to dedicate resources to where they are needed most (Marino et al., 2020). Because predictive analytics tends to be real-time data, healthcare professionals can have up-to-date information to see where the infection occurs and where to go to reduce that concern (Marino et al., 2020).
Predictive analytics (PAs) use prognostic resources to sort through large amounts of data (Kessler et al., 2020). PA improves healthcare outcomes by utilizing innovative approaches to examine realistic data to implicate theoretical conclusions (Kessler et al., 2020). Allen et al. (2023) utilized predictive analytics in their evidence-based practice (EBP) research study to identify and manage risks for patients challenged by healthcare disparities related to opioid drug overdose. For example, the decision support model encompasses PA used by healthcare clinicians to identify patterns of patients' behavior and trends that led to opioid overdose amongst patients challenged by healthcare disparities (Allen et al., 2023).
There is a 7.5%-36.4% national probability that minorities who live in impoverished communities are more likely to overdose on opioids. Moreover, there is a 5%-20% opioid drug overdose potentiation state-wise for the same vulnerable health group. Allen et al. (2023) predicted that social assessment tools identified patients predisposed to disparaging outcomes related to patterns and trends and identified high probabilities of drug overdose for nationwide minority patients living in impoverished demographic locations (Allen et al., 2023). The predictive model produced four criteria from data analytical findings: implementation capacity, preventative identification, health equity, and jurisdictional clinical guidelines (Allen et al., 2023). The predictive model fosters the prevention and mitigation of poor healthcare practices and delivery approaches that enable patients to receive adequate resources, medical treatment, and therapeutic services within prospective communities challenged by healthcare disparities (Allen et al., 2023).
Future Challenges and Opportunities of Predictive Analytics in Healthcare
Boussina et al. (2023) impose challenges to real-time data accuracy and size. A poorly integrated data analytics system will impose risks by automating the data workflow, enhancing data governance, and reducing mistakes (Boussina et al., 2023). The electronic health record is received, recorded, appropriately formatted, and saved, which ensures the quality of the (Boussina et al., 2023). Boussina et al. (2023) study identified that data duplication could impose data entry errors along with misconstrued efforts to improve the accuracy of data collection for disease prevention. Privacy concerns and ethical considerations can impose breaches in data collection (Boussina et al., 2023). During the transfer or migration of data, some vital value or information might be missed (Boussina et al., 2023). Conclusively, to ensure data quality, it is essential to pay high attention to the data during data migration (Boussina et al., 2023).
Vero:
Data science is used in healthcare to develop advanced medical devices and systems that can diagnose and treat diseases. It can also be used to personalize healthcare recommendations, predict patient outcomes, and identify potential outbreaks. Predictive analytics has the potential to revolutionize mental healthcare by identifying individuals at risk of developing mental health problems or predicting the course of existing conditions.
Early Intervention: According to Hahn et al., (2017) analyzing data like electronic health records, social media activity (with patient consent), and even wearable sensor readings, algorithms can flag people with risk factors for depression, suicide, or other mental health issues. This allows for early intervention, potentially preventing escalation of symptoms.
Personalized Treatment: Predictive models can help tailor treatment plans to individual needs. Analyzing a patient's response history to therapy or medication can guide clinicians towards the most effective approach.
Practical Application in Nursing: Imagine a nurse working in a primary care setting. They can leverage predictive analytics to: Proactively screen patients during routine checkups, identifying those at high risk for developing mental health issues based on factors like family history or social determinants of health. Tailor educational resources and mental health support referrals based on the model's predictions.
Challenges and Opportunities
While promising, there are roadblocks to consider:
· Data Privacy: Ensuring patient privacy and gaining informed consent for data collection is crucial.
· Algorithmic Bias: Models trained on biased datasets can perpetuate inequalities in mental healthcare access. Careful data selection and model evaluation are essential.
The future of predictive analytics in healthcare is bright. Early intervention and personalized treatment have the potential to significantly improve patient outcomes. By focusing resources on high-risk individuals, healthcare systems can operate more efficiently. As research progresses and ethical considerations are addressed, predictive analytics has the potential to become a powerful tool for supporting mental health and improving overall well-being.
Leadingweek5replies.docx
Leading org week 5 replies
Respond to the 4 students by expanding upon their post or offering an alternative interpretation of the patient experience measures described by your colleague as they might relate to your specific practice or organization. Include 2 referencs each
KRISTINE:
Dashboards and scorecards have become valuable tools for leaders. Scorecards comprising financial, performance, and productivity measures reported in real-time become critical resources for clinicians and leaders. Continually monitoring performance and data allows leaders to promptly make course corrections or change strategies. If the quality of care is to be improved, the data displayed must be high-quality.
The greatest challenge in using dashboards is identifying and measuring what matters and which metrics are the critical variables that accurately and comprehensively indicate value, services, and cost outcomes.
Organization X is an academic institution heavily focused on research. The organization spans multiple states and has hub campuses. This post will focus on one hub, “Y,” which provides various services and specialties to its community. Additionally, the acute care hospital is composed of 6 medical-surgical units. An inpatient and ambulatory quality nurse specialist is responsible for updating the Department of Nursing (DoN) Scorecard within the Department of Nursing.
The scorecard has three sections: Core Measures, Improving the Patient Experience, and Preventing Harm. Each section has individual clinical measures that are impacted by nursing. Patient experience is an integral component of health care delivery. Understanding the patient’s perspective is crucial to evaluating whether their care outcomes align with their values and expectations (CMS, 2020). It is important to note that patient experience and satisfaction are different. Patient satisfaction measures expectations of how their care is being delivered. Patient experience is a measure of comparing what was expected by the patient to what occurred (Nash et al., 2019).
Patient experience metrics are essential in explaining an organization’s overall assessment of quality care outcomes. The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) allows organizations to obtain this data through patient surveys post-discharge (CMS, 2023). The DoN focuses on the HCAHPS measures that nursing directly impacts, influencing the “likely to recommended” metrics. The HCAHPS measures present on the scorecard are:
· communication about medicines
· communication with nurses
· discharge information
· quietness of the environment
· responsiveness of hospital staff
These metrics are used to benchmark across the organization and with similar institutions. Targets are based on past performance. If one area is not meeting the top box score, the goal is to improve that area by two points over one year.
When reviewing these metrics, the scorecard utilizes a color scheme for easy visual management: red indicates underperformance compared to the target, and green indicates meeting or exceeding the target. The only measure that needs to be green is the quietness of the environment. Significant efforts have been put into the other four measures, which include additional training, leveraging technology, and constant observations with feedback from the nurse managers to front-line staff. The key to success is having engaged staff.
The outlying opportunity that is a continuous challenge is the quietness of the environment. This is due to the number of alarms that sound throughout the day and night, including pumps, safety alarms, patients who yell out, voices that carry, and aging facilities.
These surveys are part of the Center for Medicare and Medicaid Services (CMS) attemtps to improve how care is provided. An organization's results directly impact the reimbursement it receives based on the delivery of services and the quality of that service (CMS, 2023). When organizations are not performing well, financial consequences can result.
Crystal:
Scorecards are used to help organizations measure their performance from the patient perspective, this can be achieved through surveys, interviews, data, and by observation (Reinaldy et al., 2023). The healthcare organization scorecard that was utilized for this discussion was completed at Advent Health East Orlando in the Emergency Department (ED). Advent Health collects data through the use of Press Ganey. Press Ganey is considered a third-party vendor that pairs with patient care organizations in an effort to provide patient satisfaction data that can help the patient care settings to monitor quality improvement (North & Tulledge-Scheitel, 2019). The score card metrics include: likelihood to recommend, overall rating ED care, staff worked together, staff cared about you as a person, nurses, doctors, arrival, informed about delays, information provided about home and follow up care, waiting times for diagnostic testing, and inpatient wait times to admission. The data is displayed on a picture of a dart board using the bulls eye as the 100th percentile ranking and it decreases out towards the outer edge which is the 1st percentile ranking. The data collected showed a comparison of November – January 2023 to November – January 2024. The overall data was able to show patient’s satisfaction had increased in each area in the year 2024. For instance, the measurement that states ‘staff cared about you as a person’ ranked 82.42 in 2023 which displayed in between the 25th and 50th percentile ranking. In 2024, this same measurement calculated at 87.96 which displayed between the 50th and 75th percentile ranking. The data was collected by random surveys that are provided to patients following their ED experience. In 2023, 371 surveys were collected and in 2024 there were 412 surveys responded to in order to collect the data that was demonstrated. Based on the data provided, the patient care goals that have been set in the ED are being met to an extent, their numbers have not reached 100% satisfaction and they not, but when comparing this year to last year there was an increase in patient satisfaction for each metric. Meeting and not meeting patient satisfaction metrics can have a big impact on the functionality of a hospital. The survey data can be utilized to provide an opportunity for improvement, increase decision making, decrease costs, attempt to meet patient expectations, assess care performance, and work on effective management (Al-Abri & Al-Balushi, 2014). Focusing on patient satisfaction is important because there is so much competition amongst hospitals in the healthcare industry (Al-Abri & Al-Balushi, 2014). One implication to the patient satisfaction surveys is that it helps to implement new patient care policies and processes but it doesn’t change the actual providers or their ability to care for the patients (Al-Abri & Al-Balushi, 2014). Each provider has a specific way that they provide patient care, some are more personable while others can be more stand offish, so even though new polices and procedures may be assigned to providers that does not mean their way of delivering that care will change.
Julius:
Over 33 million hospitalizations occurred in the US in 2020, according to Morton et al. (2023). There are a lot of outpatient visits as well. Therefore, it is crucial to examine patient experience in order to evaluate the caliber of care provided to millions of Americans annually. Patient satisfaction is a subjective, individual assessment of the services received, according to Hamid et al. (2022). This paper aims to give a quick overview of a healthcare organization, explain the use of a patient experience scorecard, explain goal setting, and go over the consequences of not meeting patient experience objectives.
Description of Healthcare Organization
The focus of this discussion post is a nonprofit hospital with 220 beds located in Maryland. Accredited by The Joint Commission, this medical center specializes in delivering comprehensive care across maternity, cancer, mental health, and cardiac and vascular services.
Description of Patient Experience Scorecard
The hospital employs Press Ganey to administer patient experience surveys to individuals receiving care, both in inpatient and outpatient settings. Although the survey encompasses over 30 questions, the primary scorecard concentrates on 7 key domains: care transitions, medication communication, interactions with doctors and nurses, discharge process, hospital environment, and staff responsiveness. Each domain correlates with a distinct question from the survey, with the scorecard detailing the number of survey participants and its corresponding scores. Additionally, the scorecard states the percentile rank goal and the percentile rank indicative of achieving world class results within each domain. The scorecard provides one place for any person to see how the current score compares to target and world class results.
Established Goals
At this healthcare site, specific patient experience objectives are established, guided by benchmarks set by Press Ganey through comparative analysis with similar hospitals nationwide. As of November 2023, the hospital successfully met its targets in all domains except for care transitions, discharge procedures, and staff responsiveness. Monthly, each leader receives domain-specific results, culminating in a comprehensive review of the hospital's overall performance during main departmental leadership meetings. Additionally, all leaders have access to retrieve these results either from the Press Ganey website or the internal hospital portal, facilitating transparent communication and awareness among staff regarding the hospital's primary goals and performance metrics.
Potential Impacts of Not Achieving the Goals
The previously discussed core domains directly correlate with the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) score. This score significantly impacts the reimbursement hospitals receive for their rendered services, leading to potential rewards or penalties based on the HCAHPS score (Morton et al., 2023). Patient experience serves as a pivotal component in shaping the overall HCAHPS evaluation. Morton et al. (2023) emphasizes that higher HCAHPS scores result in increased financial rewards for hospitals. This holds particular significance in Maryland, where hospitals operate under a global pay model rather than a fee-for-service system (Blanco-Topping, 2021). Maryland hospitals receive a lump sum amount to care for patients, irrespective of the actual total care costs incurred. Consequently, these hospitals face challenges in maintaining a profit margin to reinvest in the institution if they experience substantial financial losses due to low HCAHPS scores. Therefore, achieving success or improvement in patient experience scores not only benefits the patients but also leads to significant financial advantages for the hospital.
In conclusion, hospitals should prioritize learning about patients' experiences with their care. This hospital constructs the aim goal around benchmarked metrics and puts a lot of effort into understanding how patients feel. My emphasis on the patient experience helps to guarantee that patients are getting the attention they require and merit. In the end, putting the needs of the patient first not only improves the quality of care given but also keeps hospitals financially viable.
Natalia:
Baptist Health Miami Cancer Institute (MCI) is a specialized cancer center that provides a variety of cancer treatments all under the same center. As healthcare financial reimbursement has moved to value-based, organizations strive for the most optimal outcome and experience. Due to this, patient experience has become a key quality indicator. According to Bastemeijer et al. (2019), patient experiences are defined as, “the sum of all interactions, shaped by organization’s culture, that influence patient perceptions, across the continuum of care”. Quality measure surveys are used by organization and centers like MCI to report patient experiences one is the Consumer Assessment of Healthcare Providers and Systems (CAHPS or HCAHPS). In particular, the CAHPS Cancer Care Survey Measures. The survey generates three types of measures for reporting purposes, (1) rating measures, (2) composite measures, and (3) single item measures. According to AHRQ (2020) the core items from the cancer care survey consists of six composite measures and four single-item measures, (1) getting timely appointments, care, and information, (2) how well cancer care team communicates with patient, (3) cancer care team’s use of information to coordinate patient care, (4) helpful courteous, and respectful office staff, (5) cancer care team supports patients in managing the effects of their cancer and treatment (6) involvement of family members and friend, (7) availability of interpreters, (8) patients’ rating of the cancer care team, (9) patients’ rating of overall cancer care.
Results are then publicly reported and used by the Centers for Medicare and Medicaid (CMS) for star ratings and value-based purchasing. Data is analyzed and a composite score is determined. From this, the organization develops a score card or dashboard that provides where goals based on CAPHS indicators are measured, tracked, and goals are set with benchmarks to determine improvements in quality measures. The score card contains timely appointments and care, prevention of Infections (HAIs), communication of staff, patient falls and injuries, cleanliness of environment, patient experience with cancer care team, and overall cancer care. MCI uses a platform called Tableau for data visualization and transform raw data into visual chartings and graphs creating. Baptist Health as an organization prides itself for the many accolades received for being not only the best workplace work but also for being one of the safest and ethical organization (Baptist Health, 2024). MCI falls under the Baptist Main Hospital license, and they are currently strategizing on how to improve cancer care access so that patients may receive treatment faster. The infection prevention and control team for each hospital under the Baptist Health umbrella work diligently to improve infection rates and prevent HAIs. HCAHPS or CAHPS helps the organization remain transparent and they work diligently to correct any deficiencies that are revealed in scores. Lastly not meeting the measures goals set by the metrics of CAHPS can negatively impact the organization’s reputation, quality of care, financial payout by CMS and other healthcare plans. Committees and interdisciplinary teams are also created to make sure that the goals and benchmarks are achieved.