Health Informatics Ethics Trust and Justice Discussion

Dis 2 525 Salman In the context of health informatics, which entails the gathering, utilization, and sharing of significant volumes of health data, there are principles take on an especially vital role (Goodman, 2020). The following are the foundational tenets around which ethical guidelines and the use of health informatics are built (Ölvingson et al., 2002): • Autonomy: refers to the right of individuals to determine their own course of action regarding their medical care. • Beneficence: refers to the obligation that medical professionals have to act in a way that is in the patients’ best interests. • Non-maleficence: refers to the obligation that medical professionals have to protect their patients from unnecessary damage. • Justice: access to high-quality medical treatment should be equitable and just. By following to these standards, healthcare workers are able to achieve a balance between the opposing needs of preserving the health of the general public and displaying respect when gathering and transmitting medical information (Ölvingson et al., 2002): • Collect and distribute only the minimum amount of data required to accomplish the task at hand. For instance, a healthcare professional shouldn’t gather information about a patient’s political opinions or social life if such information isn’t relevant to the patient’s medical care. • Before collecting patients’ data or sharing it with anybody else, get their informed consent first. Patients should be told about what data are being collected, how that data will be used, and who will have access to it before it is gathered. • Maintain the confidentiality and safety of patient information. In order to prevent unauthorized access, use, or disclosure of patient information, providers of healthcare services should put in place the necessary security precautions. • Employ a responsible and ethical approach while using the patient’s data. It is unacceptable for providers of medical treatment to utilize patients’ personal information for activities that are not in the patients’ best interests. For instance, a healthcare professional should not disclose patient information to other parties for the sake of marketing without first obtaining the patient’s permission. The following are some ways in which healthcare practitioners may demonstrate respect for their patients while still meeting the obligation to safeguard the health of the public as a whole (Zhang & Zhang, 2023): • Data for public health monitoring systems should only include information that is absolutely necessary for detecting and stopping the spread of diseases. Patients’ privacy and security must be protected, and they must have given their informed consent before any data is collected. • Providers of medical treatment should only release patient information to researchers for medical studies after obtaining patients’ informed permission. Additionally, they need to make sure that researchers have taken enough precautions to safeguard the information. • Healthcare providers have a duty to utilize patient data in a responsible and ethical manner while creating new treatments and services. They shouldn’t, for instance, mine the data in ways that can lead to discrimination against particular groups. References Goodman, K. W. (2020). Ethics in health informatics. Yearbook of Medical Informatics, 29(1). https://doi.org/10.1055/s-0040-1701966 Ölvingson, C., Hallberg, J., Timpka, T., & Lindqvist, K. (2002). Ethical issues in public health informatics: implications for system design when sharing geographic information. Journal of Biomedical Informatics, 35(3), 178–185. https://doi.org/10.1016/s1532-0464(02)00527-0 Zhang, J., & Zhang, Z. (2023). Ethics and governance of trustworthy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911-023-02103-9 dis 2 525 Ahmed principles of ethical guidelines and the use of health informatics Using health informatics brings up a number of ethical questions, like how to protect the privacy and confidentiality of patients, how to make sure that health data is accurate and safe, and how to use health data in a fair and equal way. Here are some of the social rules that should be followed when using health informatics: • • • • • Privacy and confidentiality: Patients have the right to privacy and confidentiality, and their health information should only be used or shared for allowed reasons. Security: Health informatics tools should be safe so that patient information can’t be accessed, used, or shared by people who shouldn’t be able to. Transparency and accountability: Healthcare providers should be clear about how they use health informatics, and they should be responsible for protecting the safety and security of their patients. Beneficence and non-maleficence: Health care providers should use health informatics in a way that helps people and doesn’t hurt them. Justice and fairness: People who work in health care should use health informatics in a way that is fair and doesn’t favor one group over another. By following these ethical rules, healthcare providers can balance the need to protect the health of the people with the need to show respect when gathering and sharing medical information. For example, healthcare providers can use health informatics to track the spread of disease and find groups of people who are at risk, but they must do so in a way that respects patient privacy and confidentiality. Health informatics can also be used to improve the standard of care for all patients, but it must be done in a fair and equal way. Here are some practical ways that people who work in health care can balance these needs • • When healthcare providers ask people for medical information, they should only ask for the information they need to give care. They should also tell people how their information will be used and shared, and they should get their permission before doing so. Healthcare providers should only share medical information with other healthcare providers that is important for the patient’s care. They should also share the information in a safe way, like by using encrypted email or a secure chat system. • • • When healthcare workers use health informatics to track the spread of disease or find groups of people who are at risk, they should take steps to protect the privacy and confidentiality of their patients. For instance, they can make the data anonymous or use general data that can’t be used to find out who a patient is. When using health informatics to improve the quality of care for all patients, healthcare providers should make sure that all patients have access to the same high-quality care, no matter their race, ethnicity, socioeconomic class, or other factors. Health care providers should also be aware that health informatics tools could have bias. The data gathered, the algorithms used to analyze the data, and the way the results are interpreted can all be biased. Health care workers should use different datasets and clear algorithms, among other things, to reduce bias in health informatics systems. By following these ethical rules and taking steps to reduce bias, healthcare providers can use health informatics to protect the health of the whole community while respecting the privacy and confidentiality of their patients. References Goodman, K. W. (2020). Ethics in Health Informatics. Yearbook of Medical Informatics, 29(1), 26–31. https://doi.org/10.1055/s-0040-1701966 Teoli, D., & Ghassemzadeh, S. (2023). Informatics Ethics. ‫ في‬StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK538512/ • • dis 2 520 Nasser Discuss the types of errors that are classified in a Root Cause Analysis. The Patient Safety Committee helps many healthcare agencies enhance quality and prevent errors. RCA systematically locates the causes of near-misses and unfavorable events to develop preventative actions. RCA teams investigate system faults that contributed to or caused near calls or unfavorable events, beyond human error. Errors must be reported honestly before RCA. A Department should vigorously encourage residents, midlevel physicians, and instructors to report unfavorable events and near misses. A risk-based triaging system should assess the report to determine RCA need. Our patient care committee of faculty and residents reviews incident reports and decides if an RCA is needed. If an RCA is needed, a small team of 4–6 people with basic expertise in the subject would be assigned (Charles et al., 2016). • Include a discussion of the accident models presented in Chapter 6 of the text. Each of these Models is an attempt to comprehend the causes of harmful causal linkages. Standard step-bystep procedures for conducting RCAs are widely documented and readily available, and the healthcare sector has adopted the RCA methodology that was well-established in other industries for accident investigation. models of accidents are used to comprehend cause-and-effect connections. the fault tree, the event tree, the bow tie model, the Swiss cheese model, the sharp end-blunt end, and the domino theory. Successful analysis involves learning from mistakes, embracing risk, and sustaining improvement, not merely finding an underlying cause. These Models demonstrate the progression of RCA procedures, emphasizing ways beyond asking “why.” This is crucial in healthcare because human performance in complicated systems is prone to errors (Shah & Godambe, 2021). Simple linear models, like Heinrich’s (1931) Domino Model found in Root Cause Analysis, and subsequently composite linear models like Reason’s Swiss Cheese Model, were essential safety tools in health care. Many were unaware that industrial safety models outside healthcare were struggling to adapt to more complicated work contexts. Resilience engineering recognizes that things have gotten more complicated and that linear models no longer explain system performance problems (Shah & Godambe, 2021). • Address how these tools could be used to analyze the errors you identified. An RCA, like an FMEA, involves a multidisciplinary team to identify the root cause of an unfavorable event or near miss. While an FMEA is a proactive approach to high-risk processes, an RCA is a reactive process conducted after an event. RCA teams, like the 5 Whys tool, ask “Why” five times to identify the root cause of an occurrence. An RCA focuses on system dysfunctions and processes, rather than individual healthcare practitioners. After designing and implementing risk reduction techniques, the RCA team evaluates the modifications and presents the outcomes to stakeholders. The team should agree on the project’s overarching goals and objectives once the problem has been identified and the project team that will involve the main stakeholders in the process has been put together. The SMART acronym stands for specific, measurable, achievable, realistic, and time-sensitive (Lau, 2015). References Charles, R., Hood, B., Derosier, J. M., Gosbee, J. W., Li, Y., Caird, M. S., Biermann, J., & Hake, M. E. (2016). How to perform a root cause analysis for workup and future prevention of medical errors: a review. Patient Safety in Surgery, 10(1), 1-5. Lau, C. Y. (2015). Quality improvement tools and processes. Neurosurgery Clinics, 26(2), 177-187. Shah, R. K., & Godambe, S. A. (2021). Patient Safety and Quality Improvement in Healthcare : A CaseBased Approach. Springer International Publishing. 10.1007/978-3-030-55829-1 dis 2 520 Ahmed Types of errors that are classified in Root Cause Analysis (RCA) Root Cause Analysis (RCA) is a way to figure out what caused an event or trouble in the first place. It is used in many fields, such as health care, industry, and flying. In RCA, mistakes can be broken down into two main categories: • • Active errors: Active errors are mistakes that people make while doing a job. They are often caused by people, like being tired, distracted, or not having enough training. Latent errors: These are mistakes that are already part of a system or process. They are often hard to find and fix because they are not directly connected to the problem or event. Active mistakes examples : 1. A patient gets the wrong drug because a nurse gave it to them. 2. When landing an airplane, a pilot makes a mistake. 3. A worker makes a mistake while running a machine at the factory. Latent mistakes include: 1. The way a medicine is packed makes it easy to mix it up with another medicine. 2. The landing gear of an airplane is not well taken care of. 3. There are not enough safety features on a workplace machine. It’s important to keep in mind that both active and latent mistakes can happen at the same time. For example, a nurse may give the wrong medicine to a patient because there was a mistake in the packaging of the medicine, and they were sidetracked by a noise (this is an example of an active error). RCA can be used to find both obvious and hidden mistakes. Then, this knowledge can be used to make and put into place changes that will stop similar events or problems from happening again. How to identify the root cause of an error When doing a root cause analysis, it’s important to think about all of the things that could have led to the event or problem. This could mean: 1. 2. 3. 4. 5. The way the world looks Those who took part The jobs that are being done The tools and gear being used The methods and ways things work Once all the important information has been collected, it can be analyzed to figure out what went wrong. Several tools and methods, such as fishbone graphs, fault tree analysis, and five whys analysis, can be used to do this. Once the root cause has been found, corrective steps can be made and put into place to stop the same problem or event from happening again. Some examples of corrective steps are: 1. 2. 3. 4. Changing how things work Giving them more training Putting into place new safety measures Getting tools fixed or replaced Conclusion RCA is an important tool for preventing mistakes and improving safety and quality in many businesses. Organizations can come up with and use effective corrective steps if they know the different kinds of errors that can happen and how to find the root cause of an error. • • References Charles, A. D., Cimino, J. J., Patel, V. L., & Rothman, D. J. (2019). Root cause analysis of clinical errors: A process for improvement. Journal of the American Medical Informatics Association, 23(3), 559-565. Cooper, J. E., & Reason, J. T. (2020). The safety of healthcare: Lessons from the aviation and nuclear industries. Wiley-Blackwell.

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