measuring the effectiveness of the intervention

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Health Medical

Description

This week, we are examining the use of an intervention using social media to inform the patients, providers, or the public about a particular topic or issue that you have selected for your Informatics Scholarly Paper. To prepare to measure concepts that are a part of your paper, you will need to select an instrument to measure them. For instance, if you plan to teach something that may be a public concern such as a communicable disease, you will need to measure the organism that is infecting the public.

Then, you would want to measure the infection rate after you provide a social media outreach campaign to the public. You could measure the knowledge level of those that participated before and after you teach them. This is normally what is done in a DNP program when the student is conducting their project. To accomplish this, it is necessary to find the best instrument or tool. To do so, reviewing the current literature through a library search is best to look for articles. In addition, you will need to view the YouTube video below and quote one of the best explanations you found useful regarding reliability and validity. Keep in mind that the measure of reliability is conducted using a Cronbach's alpha score, which should be within 0.70-0.90 for reliability of an instrument or tool to exist. If an instrument or tool is not within these parameters, it is not reliable.

Required Video-(view this before continuing)

Reliability vs Validity (2023)

Reliability vs Validity Transcript

Instruments or Tools

Select one of the instruments below that you think fits best with your topic and informatics theory. Review the corresponding peer reviewed article that explains the psychometric properties of the instrument, including the Cronbach alpha or reliability percentage. Share in your post what you have found.

Instrument: Confidence Scale (C-Scale) permission obtained from author

Article: The Confidence Scale: Development and Psychometric Characteristics

Instrument and article: Intrinsic Motivation Inventory (IMI) No permission needed

Instrument: Satisfaction With Life Scale (SWLS) No permission needed.

Instrument: Brief-Coping Orientation to Problems Experienced Inventory (COPE) No permission needed

  • Articles: 

Carver, C. S. (1997). You want to measure coping but your protocol’s too long: Consider the Brief COPE. International Journal of Behavioral Medicine, 4, 92-100. [abstract]

Instrument and Article:

Initial Post

  • For your initial post, please pick one of the instruments or tools provided, read the peer reviewed article, examine the aspects of the instrument or tool, and explain the Cronbach's alpha reliability coefficient score and usefulness. In addition, share with your peers how the instrument or tool will fit with your paper to use before and after your intervention. It may not be a perfect fit but pick one so we can go through the process.


Unformatted Attachment Preview

1 Informatics Scholarly Paper Beatriz Cruz Professor Traci Bramlett Regis College NU 710: Informatics in Healthcare March 5, 2024 2 Informatics Scholarly Paper Bridging the Gap between Patient-Generated Health Data and Electronic Health Records In contemporary health care, digital innovation has taken root paving the way for better ways of health care provision. The digital paradigm has allowed patients to become active in personal health management. Patients can generate health data and analysis through a range of digital applications and devices. Patient-Generated Health Data has become an innovative way of providing healthcare services and has been embraced by many industry actors and health institutions. A person's health state and behaviors may be better understood with the help of patientgenerated health data. Numerous industry players and healthcare organizations have welcomed this novel approach to delivering healthcare services. In addition to being ineffective, it is frequently disjointed and not connected to the conventional electronic health records (EHRs) that medical professionals utilize. With this gap, utilizing PGHD for clinical decision-making and care coordination becomes difficult as a result. The proposed research study seeks to examine how we can bridge the gap between PGHD and EHRs. The goal is to investigate the significance of PGHD integration into EHRs, the tactics being used to accomplish this integration, the obstacles to effective implementation, and the prospects for further developments in this area in the future. This will shed light on the possible advantages of this integration for patients and healthcare professionals by investigating these facets of bridging the PGHD and EHR divide. Conceptual Model The Patient-Knowledge-Base-EHR (PKBE) model is conceptual model proposed for this study. This model proposes a unique framework that will help cover the gap between EHRs and PGHDs. The model will be based on the idea that patients are active care recipients and who have 3 unique knowledge about personal health. The core of the model is founded on the patient's knowledge and skills, both explicit and tacit data/information. Observations, facts, and other verifiable information that patients have about their health are referred to as explicit knowledge, which includes a person's medical history, current symptoms, and prescription drugs used. The working of the PKBE model will be founded on the fact that there is a gap between the existing knowledge and the patients state of health captured in the EHRs systems by the healthcare providers. 4 References Tiase, V. L., Hull, W., McFarland, M. M., Sward, K. A., Del Fiol, G., Staes, C., ... & Cummins, M. R. (2020). Patient-generated health data and electronic health record integration: a scoping review. JAMIA open, 3(4), 619-627. Tiase, V. L., Hull, W., McFarland, M. M., Sward, K. A., Del Fiol, G., Staes, C., ... & Cummins, M. R. (2019). Patient-generated health data and electronic health record integration: protocol for a scoping review. BMJ open, 9(12), e033073. Arsoniadis, E. G., Tambyraja, R., Khairat, S. S., Jahansouz, C., Scheppmann, D., Kwaan, M. R., ... & Melton, G. B. (2015, January). Characterizing Patient-Generated Clinical Data and Associated Implications for Electronic Health Records. In MedInfo (pp. 158-162). Adler-Milstein, J., & Nong, P. (2019). Early experiences with patient-generated health data: health system and patient perspectives. Journal of the American Medical Informatics Association, 26(10), 952-959. Ye, J. (2021). The impact of electronic health record–integrated patient-generated health data on clinician burnout. Journal of the American Medical Informatics Association, 28(5), 10511056. Kawu, A. A., Hederman, L., Doyle, J., & O'Sullivan, D. (2023). Patient generated health data and electronic health record integration, governance, and socio-technical issues: a narrative review. Informatics in Medicine Unlocked, 37, 101153. Omoloja, A., & Vundavalli, S. (2021). Patient generated health data: Benefits and challenges. Current Problems in Pediatric and Adolescent Health Care, 51(11), 101103. 5 Jim, H. S., Hoogland, A. I., Brownstein, N. C., Barata, A., Dicker, A. P., Knoop, H., ... & Johnstone, P. A. (2020). Innovations in research and clinical care using patient‐generated health data. CA: a cancer journal for clinicians, 70(3), 182-199. 1 User Acceptance Theory in Heath Informatics Student’s Name Instructor’s Name Institution Affiliation Course Name & Code Due Date 2 User Acceptance Theory in Heath Informatics Introduction The field of Health informatics has faced rapid growth owing to the advanced interplay between technology advancement and healthcare systems. This advancement is aimed at improving patient outcomes and the overall efficiency of healthcare provision. Understanding how users interact with these technologies has become more crucial as electronic health records (EHRs), personal health devices, telemedicine, and other digital tools have proliferated within the healthcare sector. One such framework is the User acceptance theory (UAT) which seeks to explain why users accept or decline new technologies and new information. The User Acceptance Theory was developed in 2003 as a framework to understand user acceptance and utilization of information technology (IT) systems. It was created by Venkateshe et al. This theory is based on earlier studies on the variables influencing the uptake and application of new technologies, including the Diffusion of Innovation Theory and the Technology Acceptance Model (Venkatesh, 2016). This theory's central claim is that the effective deployment and use of IT systems depend heavily on user acceptance. Four key factors—perceived usefulness, perceived ease of use, attitude toward using the system, and behavioral intention to use—are identified by Venkatesh et al. as determining factors of user acceptance. The UAT theory was created through an in-depth evaluation of the literature on consumer adoption behaviors across numerous fields and disciplines. The foundations of this theory lie in three number-one additives: the era reputation model (TAM), the social cognitive concept (SCT), and the innovation diffusion concept (IDT). TAM postulates that perceived usefulness and ease of use are two key determinants for a person's reputation, even as SCT posits that character ideals, attitudes, self-efficacy, and behavior play a tremendous role in adoption selections. IDT 3 emphasizes the importance of social influence networks in creating cognizance approximately innovations among ability adopters (Venkateshe, 2012). How the theory’s basic premises fit with my chosen topic or gap. My topic seeks to examine how we can bridge the gap between Patient-Generated Health Data and Electronic Health Records. By sing The User Acceptance Theory, I will be able to demonstrate the ease of use and acceptance of patients in using the two systems and comparatively assess which system is preferred more. Using the four main constructs of the theory—performance expectancy, effort expectancy, facilitating conditions, and social influence—I will be able to understand the variations in user intention to accept or reject new technology. By incorporating multiple theoretical perspectives, UAT provides a comprehensive understanding of user acceptance behavior by considering factors that are both internal to an individual's perception and external, influenced by their environment. Conclusion In conclusion, the perception of user acceptance plays a critical role in decisions making regarding EHR adoption. Similarly, people are more likely to use health apps that are perceived as being burdensome and offering little value than those that are perceived as having obvious benefits, like making appointment scheduling or medication reminders easier. Patients are more likely to accept and make use of telemedicine platforms if they believe they will shorten their travel time or provide them with better access to specialized care. Furthermore, research has demonstrated that the attitudes of healthcare professionals regarding the use of clinical decision-support systems impact their choice to employ this type of technology. 4 References Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 157-178. Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376.
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Explanation & Answer

Attached.

Measuring the Effectiveness of the Intervention
Thesis statement: The Confidence Scale is important as it allows a person to assess the
confidence of healthcare workers when it comes to making use of various tools and information
that is available to them.
1. Measuring the Effectiveness of the Confidence Scale
a. The confidence scale (C-Scale) was the tool that was most relevant to my study as it
was initially used to help measure the degree of confidence that nurses had in
accomplishing various tasks
2. Application of the Confidence Scale to my Topic
a. The utilization of the Confidence Scale (C-Scale) holds significant promise in
bridging the gap between Patient-Generated Health Data (PGHD) and Electronic
Health Records (EHRs).


1

Measuring the Effectiveness of the Intervention

Name
Institutional Affiliation
Course Number and Name
Instructor Name
Assignment Due Date

2
Measuring the Effectiveness of the Confidence Scale
Through the assessment of the various tools, I found that the confidence scale (C-Scale)
was the tool that was most relevant to my study as it was initially used to help measure the
degree of confidence that nurses had in accomplishing various tasks. The scale is used to assess
how a...


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