[email protected]

New Order

The Innovations To Improve Outcomes Assignment Paper

The Innovations To Improve Outcomes Assignment Paper

  • Identify a systems or organizational performance problem in your current or previous workplace that may be helped by an innovation.
  • Search in the library databases for articles on healthcare innovations that may address the problem you have identified.
  • Select at least three research articles to use for this Assignment The Innovations To Improve Outcomes Assignment Paper.

    ORDER A PLAGIARISM-FREE PAPER HERE

Innovation Presentation

Imagine that you are presenting on the identified problem at a staff meeting in your current or past workplace. For the meeting, develop an evidence-based narrated PowerPoint presentation to support application of an innovation drawing from the ones you have researched.

Your narrated presentation should be 8–10 slides long and include speaker’s notes. The narration should be no longer than 10 minutes. In your presentation, address the following: The Innovations To Improve Outcomes Assignment Paper

  • Describe the problem you have identified and why it is important to address. Be specific.
  • Synthesize the three research articles you read and summarize the takeaways you can apply to your proposed innovation.
  • Explain your proposed innovation and how it would address the systems or organizational problem you have identified. Be specific and provide examples. NOTE: The response must synthesize and integrate at least two outside resources and two competency-specific resources that fully support the responses provided.
  • Describe the potential impact of the innovation on patient experience and outcomes. Be specific.
  • Explain how you would apply the innovation in the workplace, based on the research.
  • Explain how to address two types of implementation challenges:
    • those involving staff, leadership, administrators, or other stakeholders.
    • those involving systems, processes, or technology The Innovations To Improve Outcomes Assignment Paper.

With the exception of a title slide and reference slide, each slide should contain speaker’s notes and voiceover narration that reflect what you would say were you to give a live presentation.

Diagnostic error causes losses to the facility, poor patient safety ranking and diminished effectiveness of care.

Diagnostic errors are costly, harrowing for patients and, unfortunately, rather common. Newman-Toker et al. (2019) cite failures of clinical judgment as being responsible for 85% of medical misdiagnoses. This holds true also for our facility.

Misdiagnosed cancers are the most common, and given that this organization is heavily invested in palliative care, decreasing diagnostic errors will improve treatment success rates, reduce cost of care, and improve patient experience

Liberman & Newman-Toker (2018) affirm that diagnostic errors are understated. Lack of a defined tool makes measurement difficult. They recommend Symptom Disease Pair Analysis of Diagnostic Error (SPADE) as a diagnostic error measurement tool. SPADE estimates diagnostic errors by matching benign symptoms to particular serious conditions, for instance dizziness and stroke (Liberman & Newman-Toker, 2018). This method requires a large data set on symptoms and disease occurrences, as well as quick pattern matching computations. Artificial intelligence is suitable for applying SPADE to measure diagnostic errors. The Innovations To Improve Outcomes Assignment Paper

Newman-Toker  et al. (2019) categorize diagnostic errors as missed diagnosis, delayed diagnosis, misdiagnosis (wrong conclusion to symptoms and imaging), and over-diagnosis

Cancers, vascular events and infections constitute 75% of all diagnosis errors.

Accurate diagnosis for these conditions depends on correct interpretation of imaging data.

Accurately measuring diagnostic errors is good to start with, but understanding of its causes is also necessary. The causes are either cognitive or system-based.  Busby et al (2018) acknowledge cognitive biases as being behind most diagnostic errors. Causes of diagnostic errors include The Innovations To Improve Outcomes Assignment Paper

Biased interpretation of medical information

Insufficient knowledge base

Poor communication of diagnosis to patients

Laboratory test errors

Poor communication between clinicians and testing facilities

Poor integration of clinical documentation systems

Clinicians ignoring patient input

Minimal integration of diagnostic processes in clinical workflows

In ­­­2010, IBM unveiled Watson demonstrating the capacity of artificial intelligence in diagnosing cancer and recommending treatment. While standalone medical AI solutions are still unfeasible, AI is increasingly being used to enhance clinical decisions (Davenport & Kalakota, 2019)The Innovations To Improve Outcomes Assignment Paper.

I propose implementing an AI decision support tool for use by physicians and advanced nurse practitioners.

The greatest potential for artificial intelligence is in addressing cognitive causes of diagnostic error. Being dispassionate, AI is not susceptible to premature closure, affective bias (emotions to patients), anchoring (fixation on initial findings), and availability bias (from apparent familiarity)The Innovations To Improve Outcomes Assignment Paper. Premature closure is particularly notorious as benign diagnoses are quickly made for symptoms to life-threatening conditions.

Modern medical science is complex, almost exceeding the mind’s capacity. Artificial intelligence can handle such complexity (Brach, 2017). Image analysis, machine learning, and neural networks have been successfully employed in facilitating the diagnosis of colorectal cancer and melanoma. AI has also been proven effective in detecting metastasis of breast cancer to close lymph nodes (Steiner et al., 2018).

In an environment of increasing costs of healthcare, demand for quality care, profitability pressure and shortage of skilled specialists, AI sufficiently caters to the cost and quality dimensions (Morgan, 2019). With parameters set according to SPADE, the system would monitor incidences of diagnostic error, and collate the data for analytical use. AI would also control for over-diagnosis, thereby reducing cost of care.

The massive information storage and data processing capacity of AI make it useful for diagnosing complex conditions. For instance, identifying risk of heart disease in patients with high cholesterol due to familial hypercholesterolemia is problematic for physicians. Combining machine learning with screening protocols and patient medical history can increase detection probability for familial hypercholesterolemia by up to 80% (Banda et al., 2018)The Innovations To Improve Outcomes Assignment Paper. Machine learning also involves predictive processes, which can be used to monitor patients and facilitate early detection of disease or recurrence.

First, implementing the custom artificial intelligence will improve integration between existing documentation systems. The system will also boost patient engagement in the diagnostic process. Treatments will be more targeted, lowering medication costs and side effects on patients. Improved measurement and documentation of diagnostic errors will provide baseline for organizational decision making. A decrease in readmissions is anticipated, as one consequence of diagnostic errors is frequent readmissions. Other impacts are improved diagnostic accuracy from AI support and diagnostic education, and reduced costs of care given more effective preventive measures (Davenport & Kalakota, 2019)The Innovations To Improve Outcomes Assignment Paper.

One way of applying artificial intelligence in reducing diagnostic errors is by generating parallel diagnosis. In essence, the sufficiently trained system is presented with a patient’s medical history and screening results, and instructed to determine highest probability diagnosis. Comparing the resulting diagnosis with clinician’s diagnosis provides insight on possible alternative conditions. The system can also be used in diagnostic education to enhance accuracy via simulation training. The third application of AI is providing regular diagnostic performance feedback for clinicians, and identifying latent missed opportunities for diagnosis (Banda et al., 2018). The other way of employing AI is improving patient engagement in diagnosis, especially with regard to capturing medical history and handoff procedures.

As with all new things, implementing the decision support AI system is likely to face challenges. Administrative challenges (involving staff, management, leadership and other stakeholders) will be managed by: defining relationship between the system and organizational mission (Rangachari, 2018), using a champion to enhance reception among physicians, sharing pertinent information with staff, management and external parties, providing effective training, employing short term rewards to accelerate adoption, measuring system’s impact on diagnostic error rates to assess its value proposition, and inviting feedback and using it to improve the system (Cianelli et al., 2016)The Innovations To Improve Outcomes Assignment Paper.

ORDER NOW

With regard to the process and technical challenges, we should: use a competent development team, ensure ongoing technical support, embed a programmer in the facility to handle issues, monitor the system for unintended biases (Starke et al., 2021), observe strict data security protocols, have modest expectations as to the system’s capabilities, and leave diagnosis and treatment plans to physicians to determine. Artificial intelligence is still a young field, and diagnoses are highly dependent on physicians. AI has to be trained, limiting its scope. Moreover, its use is secondary to physicians for ethical reasons (Norori et al., 2021)The Innovations To Improve Outcomes Assignment Paper.