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Evidence-Based Innovation Assignment Proposal Paper

Evidence-Based Innovation Assignment Proposal Paper

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Initially, the notion of innovative or disruptive change seems to have a negative meaning. But bringing positive change has everything to do with it. People typically become so accustomed to doing things in a specific manner that it practically comes naturally to them. This holds true for healthcare in general and nursing practice in particular. The desire of people to preserve the status quo is understandable given that it is what they are accustomed to. This explains why employees in institutions typically oppose change ideas. Here, “disruptive innovation” or “disruptive change” refers to a concept that transforms an existing process in ways that everyone involved could not have predicted (Herrmann et al., 2018; Sounderajah et al., 2020)Evidence-Based Innovation Assignment Proposal Paper. The novel concept or invention is viewed as “disrupting” the status quo and rewiring processes. Many will be against it just because it would force them out of their comfort zones. This is true even when the concept or invention in question aims to revolutionize the way healthcare is provided, in this particular context. The purpose of this paper is to look at disruptive innovation in the healthcare context, in particular innovations to decrease or prevent accidental patient falls in a medical-surgical unit.

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Scholarly Examples of Disruptive Innovations that Improved Healthcare

Although disruptive innovation brings about groundbreaking changes that go against accepted conventions, it also involves risk-taking and expenditure. The goal of incremental innovation is to continuously improve products and services by making small, steady breakthroughs that maintain them at the forefront of the market (Herrmann et al., 2018; Sounderajah et al., 2020). Disruptive innovation, or transformation, differs from incremental innovation in that the former presents a novel, dramatic concept or product that totally alters behaviors, while the latter progressively enhances preexisting concepts and goods. A disruptive transformation has a significant impact on a business’s operations. This is due to the fact that it compels the staff and management to adopt new methods of operation. Because it is in our nature to reject change and hold onto comfort, this is not a simple task.

As an illustration, consider a healthcare facility that, in order to comply with the HITECH and ARRA laws, decides to buy an electronic health record (EHR) system from a vendor after previously using paper documents. This would be a disruptive transition because information about patients will now be collected in an entirely new, electronic manner, requiring nurses and other workers to undergo training and upskilling (Herrmann et al., 2018). As such, this change will totally disrupt operations within the organization for a while as people get accustomed to it and learn it.

The second example of disruptive innovation in healthcare – much more pertinent to the subject of this paper’s innovation proposal – involves the use of wearable sensors to prevent falls in vulnerable patients (Silva de Lima et al., 2019; Subramaniam et al., 2022). This intervention departs from the usual traditional evidence-based fall prevention intervention bundle of hourly rounding, use of bedside alarms, and closed-circuit television (CCTV) monitoring (Falcão et al., 2019; Gavaller et al., 2019)Evidence-Based Innovation Assignment Proposal Paper. The sensors are worn by the patient (especially the elderly) in the sole of the foot, ankle, or hand. These then detect and send information (data) in real time about the patient’s position, spatial orientation, gait, and motion to the designated healthcare provider. This way prevention is instituted before a fall actually occurs.

How the Nurse Innovator Demonstrates a Role in Conceptual Model

The philosophical foundation of nursing is based on the various roles that nurses perform, starting with the primary duty of patient advocacy. However, advocating for patients as a nurse is a difficult job to do. In order to support the accomplishment of client advocacy, the nurse must juggle multiple interrelated duties. Three specific ones serve as representations for these functions. These are the functions of the nurse as an investigator (detective), a scientist, and an environmental manager where healing is promoted. These three duties of the nurse are supported by a number of Master of Science in Nursing (MSN) model elements from Western Governors University’s (WGU) “Nursing Programs Conceptual Model.” The role of the nurse as a detective, within the given conceptual model, is discussed below. Evidence-Based Innovation Assignment Proposal Paper

The Nurse as a Detective or Investigator

Solving cases with obscure causes is the usual role of a detective or investigator. For a nurse, this is precisely what they do. It should be kept in mind that this job is examined via the lens of patient advocacy, as was covered in the opening section above. For example, the nurse might become curious if a patient had not responded to treatments as planned. This would set off their investigative instinct, making them wonder whether there was a reason behind this. It is possible that the patient could also be feeling that their autonomy – a bioethical principle – has been violated because they do not get treatment that is culturally appropriate. It might also be the result of a behavior or quality that they have not felt confident enough to disclose to any member of the medical staff yet. Therefore, the nurse’s investigative work would be necessary to make this revelation.

Thoughtful patient-centered care is one of the MSN conceptual frameworks that can be used to illustrate how a nurse can utilize the role of detective in practice. In our increasingly diversified culture, this is essentially holistic, culturally aware patient care that honors the patient and their individual preferences. Through the use of compassion and empathy, the nurse should be able to gain the patient’s trust and be trusted with knowledge that they would not have known otherwise. Establishing trust with their subject is the foundation of a detective’s work.

The use of technology and informatics is another MSN model component that could be utilized to demonstrate how the function of the nurse as detective can be applied in nursing practice (McGonigle & Mastrian, 2021). The modification and archiving of patient data in a way that makes it easily available through the use of cutting-edge, modern technologies is the focus of nursing informatics. The electronic health record, or EHR, is the ideal example of this in modern practice. The nurse can take on the role of a detective by using technology to learn about a patient’s social, family, medical, surgical, and treatment history through a process called as data mining. Evidence-Based Innovation Assignment Proposal Paper

From an investigative perspective, utilizing informatics is preferable to reviewing paper records since it eliminates the need for the nurse to communicate with the medical records specialist, providing greater anonymity. To gain permitted access to the patient’s data, they only need to utilize their special system access credentials to log onto the unit’s computer or another distant computer (McGonigle & Mastrian, 2021). It is useful to have system components like the electronic medical record (EMR) and patient data management system (PDMS). They will make it possible for the nurse to learn about the patient’s health status and other information that might help to explain a specific occurrence promptly and effectively.

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Using Big Data for Innovation

Benefit

Big data is the term for the vast digital flood of data which is generated in the healthcare sector as an assortment of forms and at a rate that keeps increasing (Wang et al., 2018). According to Gavrilov et al. (2019), a healthcare data warehouse is an organized, centralized archive for all relevant healthcare data gathered from several sources, such as prescriptions, demographic data, imaging results, laboratory test results, and more. EHR and EMR systems are the main suppliers of this data. For the purpose of providing physicians, nurses, and investigators with reliable, evidence-based health information, clinical data mining is a collection of data science methods and resources (Alharthi, 2018)Evidence-Based Innovation Assignment Proposal Paper. Healthcare data mining techniques are used in several health-related disciplines, including biotech, pharmaceutical research, and medical science.

Healthcare organizations can profit from healthcare data mining in a number of ways. Nurses and doctors working with the organization can make more informed decisions about patient care with the use of data mining. The methods of data mining can also be used by payers, like insurance companies, to uncover provider fraud involving insurance. Even better, data mining can be used by physicians and nurses to find evidence-based patient treatment strategies that work (Alharthi, 2018; Najjar et al., 2018; Wang et al., 2018)Evidence-Based Innovation Assignment Proposal Paper. Not to mention, data mining keeps care coordination firmly established and keeps it from fragmenting.

Challenges

Big data has been developing, bringing with it difficulties and issues brought on by the rapid expansion of medical data. Massive data presents numerous issues for analysis, storage, and recovery due to its continual transformations. The large and tremendous volume of material makes conventional or standard database systems unsuitable for processing, storing, and retrieving data (Awrahman et al., 2022). Big data problems fall into four primary types that are typically encountered in healthcare organizations: Large amounts of unstructured data, such as handwritten text and natural language, are included in large clinical data. The analysis, integration, and storage of clinical big data present a fair bit of challenge. Sharing structured data among entities is inadequate while sharing unstructured data among organizations is more difficult. Essentially, mining such a vast volume of unstructured data is a huge task.

ANA Code of Ethics to Guide Ethical Use of Big Data

The American Nurses Association (ANA) developed the Code of Ethics for nurses after realizing that professional nursing needed guidelines for behavior in practice (Morris, 2023). When using technology and data, nurses have an obligation to maintain patient privacy and confidentiality, according to the American Nurses Association’s (ANA) Code of Ethics (Ienca et al., 2018). It is imperative for nurses to guarantee that patients’ safety, autonomy, and dignity are not jeopardized by any technology or big data handling.         

Using New Technology for Innovation

 

As technological innovation has advanced, the idea of “smart healthcare” has progressively gained prominence. The latest generation of information technologies, including big data, cloud computing, artificial intelligence, and the internet of things (loT), are being used in smart healthcare to completely replace the existing medical system and provide more individualized, convenient, and cost-effective care (Tian et al., 2019)Evidence-Based Innovation Assignment Proposal Paper. The backbone of smart healthcare is current biotechnology combined with information technologies such as IoT, mobile Internet, cloud computing, big data, 5G, microelectronics, and artificial intelligence. Artificial intelligence (AI) is without a doubt one of the most important developments in healthcare technology to date. The development of remote care is also being driven by telemedicine, and the usage of extended reality in medical settings is becoming more and more popular.

Proposed Disruptive Innovation to Improve Healthcare Outcomes

Unintentional patient falls are a quality improvement concern that must be addressed in every healthcare setting. This is a serious problem, particularly for senior care facilities that provide long-term care. It is well known that individuals 65 years of age and older are more likely to experience unintentional falls at home or in an institution of care (Guirguis-Blake et al., 2018). However, patients in the medical-surgical units of hospitals regardless of age are also at an increased risk of suffering unintentional falls. These need to be prevented as they qualify as sentinel events.

Various interventions (usually in a bundle) have been researched over time and found to be effective in the prevention of accidental falls in healthcare facilities. These include intentional or purposeful hourly rounding by nurses in a shift, the use of bedside alarms, use of CCTV monitoring, patient education, and nurse training amongst others (Gavaller et al., 2019; Guirguis-Blake et al., 2018). However, these have been used for some time now and it was time that some disruptive technological innovation were implemented to better prevent patient falls. It is in this light that the proposed disruptive innovation to improve healthcare outcomes in the context of falls is the use of wearable sensors (Mooyeon et al., 2021; Silva de Lima et al., 2019; Subramaniam et al., 2022). These are technological innovations underpinned by artificial intelligence (AI)Evidence-Based Innovation Assignment Proposal Paper.

Description of Proposed Healthcare Organization

            The proposed healthcare organization is a tertiary hospital located in a metropolitan area and which has a large medical-surgical unit. The unit has adults but also the elderly who are sick with some having undergone surgery. It has recently seen the rise in incidents of accidental falls despite the use of the traditional preventive bundle of hourly rounds, bedside alarms, CCTV, patient education, and nurse training. This rate has now reached the national benchmark of 3.44 falls per every 1,000 hospital stays (Venema et al., 2019). It is feared that it will soon surpass this national average and reach unacceptable crisis levels. This is why the disruptive technological innovation of wearable sensors needed to be implemented without delay.

How the Innovation Supports Organizational Goal or Strategy

The innovation of wearable sensors supports the organization’s goal and strategy of providing the best quality of care using all resources available at its disposal. The vision of the organization is to be the leading center of excellence for evidence-based practice and exemplary nursing care among competitors in the same category and class. Implementing the use of wearable sensors at this point will thus improve patient outcomes in the medical-surgical unit and give the organization a competitive advantage. Evidence-Based Innovation Assignment Proposal Paper     

Relevant Sources Summary Table  

Table 1

Relevant Sources Summary Table

 

Scholarly Peer-Reviewed Sources

Published in Past 5 Years

 that

Support the Proposed Innovation

Summary of Findings Relevant to Proposed Innovation

 

 

 

 

Evidence Strength

Level I–VII

 

 

 

Evidence

Hierarchy

  APA formatted scholarly reference with a DOI or retrievable link.

 

 

 

 

 

Present a detailed summary of the findings and

how the findings support the proposed innovation.

Provide an in-text citation for each summary

and include each source in Reference list.

 

 

 

Refer to

WGU Levels of Evidence

 

 

 

SCHOLARLY SOURCE 1 Silva de Lima, A.L., Smits, T., Darweesh, S.K.L., Valenti, G., Milosevic, M., Pijl, M., Baldus, H., M de Vries, N., Meinders, M.J., & Bloem, B.R. (2019). Home‐based monitoring of falls using wearable sensors in Parkinson’s disease. Movement Disorders, 35(1), 109-115. https://doi.org/10.1002/mds.27830

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Based on factors including age, gender, comorbidity, and living circumstances, the researchers compared all 2,063 senior people who self-reported having Parkinson’s disease (PD) to 2,063 elderly people who did not have PD (controls). They examined fall occurrences that were recorded at home using a wearable sensor. The wearable falls detector was used to automatically collect fall occurrences, or the device’s button was pressed to register each fall (Silva de Lima et al., 2019). Using a 2.5-year timeframe and an average follow-up of 1.1 years, they extracted fall events. The findings demonstrated that having Parkinson’s disease almost doubles the risk of falling in real-life situations.  

Level II

Prospective cohort study (true experimental study)
SCHOLARLY SOURCE 2 Subramaniam, S., Faisal, A.I., & Deen, M.J. (2022). Wearable sensor systems for fall risk assessment: A review. Frontiers in Digital Health, 4(921506), 1-20. https://doi.org/10.3389/fdgth.2022.921506 Because wearables are small, inexpensive, and easy to use, they are perfect for tracking health because they can collect physiological data while people go about their regular lives and monitor activity continuously (Subramaniam et al., 2022). Wearable solutions also avoid the drawbacks of typical laboratory-based activity monitoring systems, such as monitoring in unfamiliar environments, requiring a huge setup, or affecting normal gait when the user is aware that they are being monitored. There are several different types of wearable health monitoring systems available, such as non-wearable wireless systems, non-wearable phone-based systems, non-integrated wearable systems, and fully integrated wearable systems. Level V Review
SCHOLARLY SOURCE 3 Mooyeon, O-P., Doan, T., Dohle, C., Vermiglio-Kohn, V., & Abdou, A. (2021). Technology utilization in fall prevention. American Journal of Physical Medicine and Rehabilitation, 100(1), 92-99. https://doi.org/10.1097/PHM.0000000000001554 The review focuses on the results of fall reductions, cost, and other advantages while providing an overview of these technology-based applications in diverse settings. Studies indicate that about one-third of falls are avoidable, and there are numerous fall prevention strategies available (Mooyeon et al., 2021). Utilizing efficiency, accessibility, and dependability, technology-based solutions have recently been implemented in the healthcare industry to achieve better patient care outcomes and experiences. Numerous applications of technology are used in fall prevention, such as wearable sensors, robotics in home environment evaluation, exergames and virtual reality, video monitoring and alarm systems, predictive and prescriptive analytics using big data, and personal coaching. Level V Review
SCHOLARLY SOURCE 4 Liu, J., Li, X., Huang, S., Chao, R., Cao, Z., Wang, S., Wang, A., & Liu, L. (2023). A review of wearable sensors based fall-related recognition systems. Engineering Applications of Artificial Intelligence, 121(0). https://doi.org/10.1016/j.engappai.2023.105993

 

It is true that a wearable-based fall-related recognition system (WFRS) makes it easier for fallers to foresee, detect, and classify fall incidents (Liu et al., 2023). A somewhat thorough introduction to WFRSs from the standpoint of sensor types and recognition algorithms has been given by earlier works. Though these investigations offer a clear technological direction, a barrier for recently interested investigators is determining which technology is acceptable for each stage of the investigation. Level V Review
SCHOLARLY SOURCE 5 Warrington, D.J., Shortis, E.J., & Whittaker, P.J. (2021). Are wearable devices effective for preventing and detecting falls: An umbrella review (a review of systematic reviews). BMC Public Health, 21(2091), 1-12. https://doi.org/10.1186/s12889-021-12169-7 From the time of their founding until April 2019, the following databases were searched for systematic studies evaluating the efficacy of wearable technology in the detection of falls: MEDLINE, Embase, CINAHL, and the Cochrane Database of Systematic Reviews (CDSR). This review contained seven systematic reviews. The best accuracy for falls detection, according to the results, seems to come from positioning sensors on the trunk, foot, or leg (Warrington et al., 2021). The accuracy, specificity, and sensitivity of these devices are all increased when many sensors are used. Level I Systematic review

 

Synthesis of Literature with Themes from Five Sources in Relevant Sources Table

            The overriding conclusion from the five studies represented in the above evidence table is that wearable sensors are indeed an effective disruptive innovation in healthcare against patient falls. Emerging themes are that they are safe, effective, affordable, and convenient. Some of the studies mention non-wearable alternatives but these have their drawbacks affecting effectiveness.

Evidence that Supports the Proposed Innovation

            The evidence that supports the proposed intervention is fund in the above five studies summarized in the above evidence table. They range from level I-V in the hierarchy of evidence. They are Warrington et al. (2021), Liu et al. (2023), Subramaniam et al. (2022), Mooyeon et al. (2021), and Silva de Lima et al. (2019)Evidence-Based Innovation Assignment Proposal Paper.

Reflection on My Role as an Advanced Professional Nurse Innovator  

            As an advanced professional nurse innovator, this project on disruptive innovation in healthcare has challenged me to be more inquisitive about current practice. This way, I will be a better nurse innovator by using the clinical inquiry route to come up with novel evidence-backed interventions supported by scholarly literature.

Two Strategies Used by Nurse Innovator to Support an Innovative Culture in Healthcare

Two strategies that could be used by the nurse innovator to support an innovative culture in healthcare are:

  1. Maintaining membership of professional nursing organizations, such as the American Nurses Association (ANA). This allows access to the latest cutting-edge research on innovative healthcare technologies by peers.
  2. Continuous professional improvement by embracing lifelong learning, frequent training and retraining, as well as upskilling. Evidence-Based Innovation Assignment Proposal Paper

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References

Alharthi, H. (2018). Healthcare predictive analytics: An overview with a focus on Saudi Arabia. Journal of Infection and Public Health, 11(6), 749-756. https://doi.org/10.1016/j.jiph.2018.02.005

Awrahman, B.J., Fatah, C.A., & Hamaamin, M.Y. (2022). A review of the role and challenges of big data in healthcare informatics and analytics. Computational Intelligence and Neuroscience, 0(0), 1-10. https://doi.org/10.1155/2022/5317760

Falcão, R.M.M., Costa, K.N.F.M., Fernandes, M.G.M., Pontes, M.L.F., Vasconcelos, J.M.B., & Oliveira, J.S. (2019). Risk of falls in hospitalized elderly people. Revista Gaúcha de Enfermagem, 40(e20180266), 1-8. http://dx.doi.org/10.1590/1983-1447.2019.20180266

Gavaller, M., Gavaller, M., & Oh, H. (2019). Impact of bed alarm removal and implementation of hourly rounding to reduce falls. Journal of the American Medical Directors Association, 20(3), B19. https://doi.org/10.1016/j.jamda.2019.01.080

Gavrilov, G., Vlahu-Gjorgievska, E., & Trajkovik, V. (2019). Healthcare data warehouse system supporting cross-border interoperability. Health Informatics Journal, 0(0), 1-12. https://doi.org/10.1177/1460458219876793

Guirguis-Blake, J., Michael, Y., Perdue, L., Coppola, E., & Beil, T. (2018). Interventions to prevent falls in older adults. JAMA, 319(16), 1705. https://doi.org/10.1001/jama.2017.21962

Herrmann, M., Boehme, P., Mondritzki, T., Ehlers, J.P., Kavadias, S., & Truebel, H. (2018). Digital transformation and disruption of the health care sector: Internet-based observational study. Journal of Medical Internet Research, 20(3), http://dx.doi.org/10.2196/jmir.9498

Ienca, M., Ferretti, A., Hurst, S., Puhan, M., Lovis, C., & Vayena, E. (2018). Considerations for ethics review of big data health research: A scoping review. PLoS ONE 13(10): 1-15. https://doi.org/10.1371/journal.pone.0204937

Liu, J., Li, X., Huang, S., Chao, R., Cao, Z., Wang, S., Wang, A., & Liu, L. (2023). A review of wearable sensors based fall-related recognition systems. Engineering Applications of Artificial Intelligence, 121(0). Evidence-Based Innovation Assignment Proposal Paper https://doi.org/10.1016/j.engappai.2023.105993

McGonigle, D., & Mastrian, K.G. (2021). Nursing informatics and the foundation of knowledge, 5th ed. Jones & Bartlett Learning.

Mooyeon, O-P., Doan, T., Dohle, C., Vermiglio-Kohn, V., & Abdou, A. (2021). Technology utilization in fall prevention. American Journal of Physical Medicine and Rehabilitation, 100(1), 92-99. https://doi.org/10.1097/PHM.0000000000001554

Morris, G. (2023, September 25). Nursing code of ethics explained. Nurse Journal. https://nursejournal.org/resources/nursing-code-of-ethics/ Evidence-Based Innovation Assignment Proposal Paper

Najjar, A., Reinharz, D., Girouard, C., & Gagné, C. (2018). A two-step approach for mining patient treatment pathways in administrative healthcare databases. Artificial Intelligence in Medicine, 87(0), 34-48. https://doi.org/10.1016/j.artmed.2018.03.004

Silva de Lima, A.L., Smits, T., Darweesh, S.K.L., Valenti, G., Milosevic, M., Pijl, M., Baldus, H., M de Vries, N., Meinders, M.J., & Bloem, B.R. (2019). Home‐based monitoring of falls using wearable sensors in Parkinson’s disease. Movement Disorders, 35(1), 109-115. https://doi.org/10.1002/mds.27830

Sounderajah, V., Patel, V., Varatharajan, L., Harling, L., Normahani, P., Symons, J., Barlow, J., Darzi, A., & Ashrafian, H. (2020). Are disruptive innovations recognized in the healthcare literature? A systematic review. BMJ Innovations, 0(0), 1-9. https://doi.org/10.1136/bmjinnov-2020-000424

Subramaniam, S., Faisal, A.I., & Deen, M.J. (2022). Wearable sensor systems for fall risk assessment: A review. Frontiers in Digital Health, 4(921506), 1-20. https://doi.org/10.3389/fdgth.2022.921506

Tian, S., Yang, W., Le Grange, J.M., Wang, P., Huang, W., & Ye, Z. (2019). Smart healthcare: Making medical care more intelligent. Global Health Journal, 3(3), 62-65. https://doi.org/10.1016/j.glohj.2019.07.001

Venema, D.M., Skinner, A.M., Nailon, R., Conley, D., High, R., & Jones, K.J. (2019). Patient and system factors associated with unassisted and injurious falls in hospitals: An observational study. BMC Geriatrics, 19(348), 1-10. https://doi.org/10.1186/s12877-019-1368-8

Wang, Y., Kung, L., & Byrd, T.A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3–13. https://doi.org/10.1016/j.techfore.2015.12.019

Warrington, D.J., Shortis, E.J., & Whittaker, P.J. (2021). Are wearable devices effective for preventing and detecting falls: An umbrella review (a review of systematic reviews). BMC Public Health, 21(2091), 1-12. https://doi.org/10.1186/s12889-021-12169-7 Evidence-Based Innovation Assignment Proposal Paper

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