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Whether or not to give the data out depends on the following factors:
* Ethical review committee approval: The research team must have approval from an ethical review committee to conduct the study.
* Data sharing agreement: There must be a data sharing agreement between the research group and my organization. This agreement should outline the specific ways in which the data can be used and the steps that the research team must take to protect the confidentiality of the data.Since the requested data includes personal identifying information, such as addresses and contact numbers, I would like to:
* Review the study design to see if these data are necessary for the study.
* Require the research team to sign a data usage agreement to protect the confidentiality of the data.
* Require the research team to have consent forms signed by the patients.
* Review the research team’s data use at each stage of the study progress. -
As a public health information professional, I definitely can’t disclose my friend’s husband HIV status to my friend without his consent. The husband has the right to maintain confidentiality of his health status. On my friend side, whether she should know her husband HIV status depends on moral and legal obligations. As her husband legal sexual partner, her husband is morally obligated to let his spouse know of his status as it can not only affect her health but also their future family planning. The husband’s doctor or the medical center where he did HIV testing or getting treatment should also be responsible to give him counseling about HIV and getting his sexual partners tested for HIV too. However , final decision to let his wife and partners know of his HIV status still lies in the husband’s hand. We medical professionals and public health professionals cannot disclose the information without his consent. If there is any legal obligation to disclose status to spouses and I am certain that my friend did not know of her husband HIV status, I should contact medical center or doctor responsible for the husband’s HIV testing and treatment to inform my concern regarding my friend health and that she is in blind about her husband HIV status. It should be their responsibility to let her know. And I shouldn’t be telling my friend directly as a public health professional who is neither involved in treatment and care of the husband and my friend, since it will be breaching confidentiality and privacy of the patient.
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Based on ADKAR model, I honestly think my previously organization did well in all the aspects particularly in the knowledge, ability and reinforcement aspects.
Awareness: The organization created awareness of the need for the app by conducting meetings with volunteers and health centers to explain the benefits of the app.
Desire: The organization created a desire for the app by emphasizing the benefits of the app to volunteers and health centers, such as improved contact tracing rates and reduced reporting errors.
Knowledge: The organization provided volunteers and health centers with the knowledge and skills they needed to use the app by conducting training sessions.
Ability: The organization gave volunteers and health centers the opportunity to practice using the app by providing them with access to the app and by providing support to them when they were using the app.
Reinforcement: The organization reinforced the new behaviors by providing feedback to volunteers and health centers on their use of the app. -
My previous example of mobile app for MDR-TB contact screening and tracking is a good example of a successful system. The app has the potential to significantly improve MDR-TB contact screening and tracking in resource-limited settings. The program found significant improvement in contact trancing activity completion rates and referral rates, and also the reduction in reporting error.
Here are some of the factors that contributed to the success of the app:
* **Data:** The app uses an algorithm to identify suspects or determine if they require other tests, such as a CXR or sputum testing. This algorithm is based on global and local criteria for identifying suspects, and it is frequently updated. This ensures that the app is using the most up-to-date information to identify suspects, which leads to better outcomes.
* **Cost:** The app is relatively inexpensive to develop and implement. This makes it a feasible solution for resource-limited settings.
* **Operation:** The app is easy to use for both volunteers and patients. Volunteers can go to an MDR-TB patient’s home to record the household contacts and their neighbors/close contacts. After entering an individual’s sociodemographic information, TB clinical symptoms, and risk factors, the app uses an algorithm to identify suspects or determine if they require other tests, such as a CXR or sputum testing.
* **Design:** The app is designed to be used by volunteers with low levels of education and experience with mobile apps. The app is also designed to be used in resource-limited settings, where there may be limited access to internet and electricity.
* **People:** The program trained volunteers on how to use the app. This training was essential for the success of the app, as it ensured that volunteers were able to use the app effectively. -
I don’t have a chance of experiencing anyDecision support systems DSS in my previous works. So, I would like to share about a DSS using at one of the medical centers in Taiwan when I was on hospital tour as industrial experience during my graduate study. DSS was used in the hospital to implement clinical pathways for specific diseases, such as dengue hemorrhagic fever (DHF) and septic shock. They integrated DSS with the electronic medical record (EMR) system to provide clinicians with real-time guidance on how to manage patients according to the clinical pathway.
For example, when a patient with hematemesis and melena comes to the emergency room (ER), the DSS can be used to assess the patient’s condition and generate a personalized care plan based on the clinical pathway. The care plan may include specific orders for examinations, investigations, and treatments.
As the patient’s data is entered into the EMR system, the DSS can be used to monitor the patient’s progress and identify any deviations from the care plan. For example, if the patient’s hemoglobin level drops below a certain threshold, the DSS can alert the clinician to the need for a blood transfusion. The DSS can also calculate the amount of blood transfusion needed based on the patient’s hemoglobin level and the clinical pathway.
By using DSS, hospital can improve patient safety by ensuring that clinicians follow the clinical pathway, improve quality of care by providing them with real-time guidance on how to manage patients according to the clinical pathway, and reduce costs by preventing adverse events and improving the efficiency of care.
Factors that might influence the decision support system implementation in an organization are high cost of implementation and maintenance, complexity, integration with other healthcare systems and clinicians work flow.
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I believe using ICD-10 is for standardization, having a common language/classification system to communicate between different healhcare organizations. It could be ICD-10 or other standard medical classification system. By not using ICD-10 by all hospitals in a country, there will be difficulties in communicating and sharing data among them since there is no common standard among hospitals which could lead to issues in data sharing, impact the usability of data for statistics and research, complicate the billing and insurance claims, and could even cause error in treatment. Overall, standardization is essential for improving the efficiency, effectiveness, and safety of healthcare.
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I agree with the finding that EMRs are one of the top leading causes of physician burnout. I have heard many complaints from health officers about using EMRs, including:
EMRs are time-consuming and cumbersome. Physicians often have to spend hours each day documenting patient information and completing administrative tasks in the EMR, which takes away from the time they can spend with patients.
EMRs are often poorly designed and difficult to use. This can lead to frustration and errors .EMRs might be a leading cause of physician burnout, but they are also essential tools for healthcare providers. To reduce EMR-related burnout, there are some things we/hospitals do.
Make user-friendly EMR system designs.
Reduce the amount of data that physicians need to enter into the EMR.
Give physicians more control over their EMR workflows.
Provide physicians with trainings on how to use their EMR systems effectively.Healthcare organizations should also provide physicians with support and resources to help them cope with burnout. This could include things like access to mental health professionals, stress management training, and flexible work arrangements.
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Thanks for sharing! I have been using Google fit for a while now. I found it much easier to track my lifestyle habits. With the combination of smart watch, it’s very helpful to also monitor activities and some vital signs (heart rate, rhythm, etc.), and sending reminders when there is abnormal findings (although not very accurate). I believe with the improvement of chips and sensors technologies, these kinds of apps and wearable devices can play a huge role in personalized medicine later.
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The INGO I previously worked for developed a mobile app for multi-drug resistant tuberculosis (MDR-TB) contact screening and tracking to accelerate referral and project reporting for an MDR-TB patient care program. This MDRTB care program trains volunteers to provide DOT to new MDR-TB patients and other activities such as contact tracing, screening and referral, etc., and volunteers report back to health centers and the program using paper forms. This approach has a number of challenges, including: low surveillance of MDR-TB contacts and identifying the suspects, and inefficiencies in data collection, storage, retrieval and poor data quality. The purpose of the app is to help increase case detection and enrollment in treatment through improved screening, documentation, and referral practices. The app allows volunteers to go to an MDR-TB patient’s home to record the household contacts and their neighbors/close contacts. After entering an individual’s sociodemographic information, TB clinical symptoms, and risk factors, the app uses an algorithm to identify suspects or determine if they require other tests, such as a CXR or sputum testing.
The app has the potential to significantly improve MDR-TB contact screening and tracking in resource-limited settings. The program found significant improvement in contact trancing activity completion rates and referral rates, and also the reduction in reporting error. However, the development and implementation of the app presented a number of challenges. First, deciding on the rules of the algorithm was difficult since there are global and local criteria for identifying suspects, and these criteria are frequently updated. Second, training volunteers. A significant number of volunteers are unfamiliar with mobile apps and technologies, especially older volunteers with low education levels.
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Genomic data from scientific experiments by pharmaceutical companies is a type of Big Data that fits into the 7Vs of Big Data characteristics in the following ways:
Volume: Pharmaceutical companies generate a massive amount of genomic data from their scientific experiments. This is because they need to sequence the DNA of millions of patients and healthy individuals in order to identify genetic variants that are associated with diseases.
Variety: Genomic data is very varied. It includes data from a variety of sources, such as blood samples, tissue samples, and tumors. It also includes data from a variety of technologies, such as DNA sequencing machines and microarrays.
Velocity: Genomic data is generated at a very high velocity. This is because pharmaceutical companies are constantly conducting new scientific experiments.
Veracity: Genomic data can be challenging to verify. This is because it is often generated by complex sequencing machines and algorithms.
Value: Genomic data is very valuable. It can be used to develop new drugs, diagnose diseases, and predict a person’s risk of developing certain diseases.
Variability: Genomic data can be very variable. This is because the amount of data that is generated on a given day can vary depending on the number of experiments that are being conducted.
Visualization: Genomic data can be visualized in a number of ways, such as through charts, graphs, and heatmaps. Visualization can help to make genomic data more understandable and actionable. -
According to Shaw T, et al., eHealth encompass three main domains: 1) health in hand; referring to being able to access to health information as and when needed, 2) interacting for health; providing multiple ways of communication among providers and consumers, and 3) data enabling health; which covers design and implementation of technologies and healthcare data management cycle.
eHealth is very broad and is a rapidly growing field. It includes a wide range of services and applications, such as Electronic health records (EHRs), Mobile health (mHealth), telehealth and telemedicine, Health informatics and wearable devices. As ICTs become more sophisticated, I think eHealth services and applications have the potential to revolutionize healthcare, the way of healthcare delivery, access, in a near future. -
I have learnt a lot from the video and also from you all. As non-IT person, I have only experienced phishing attacks in the form of emails. But in the past 1 or 2 years in Myanmar, I have found a lot of victims of phishing attack with elements of social engineering and potentially mobile malware. The attackers used deception to trick users into installing a malicious mobile app, which, in turn, allowed them to steal sensitive information and initiate unauthorized transactions. The attackers set up a Facebook page selling services or pretending official Facebook page of banks to lure potential victims. They then encouraged customers to install a mobile application, promising discounts as bait or ask for banking information claiming it is needed to secure the bank accounts. Once users installed the mobile application, it requested certain permissions. After gaining these permissions, the app could have started harvesting sensitive data from the user’s device. With access to sensitive data, the attackers might have used the stolen information to initiate unauthorized transactions, including mobile banking transfers, without the victims’ knowledge or consent. It’s also possible that the app contained malware designed to facilitate unauthorized access to the user’s mobile banking application. Malware can silently run in the background, allowing attackers to take control of the device.
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In this case scenario, choosing a cloud server for the patient appointment application is a cost-effective and scalable solution adding minimal workload to IT officer. Cloud servers are typically more cost-effective than purchasing and maintaining physical hardware. As hospital application grows and more patients start using it, cloud server can easily scale up without the need to purchase additional hardware. Cloud providers shoulder the responsibility for hardware maintenance, security updates, and data backups, hance reducing workload of IT officer.
For the cloud computing service model, I think opting for a PaaS model would better streamline development and deployment while allowing the hospital’s IT officer to focus on other critical tasks. I would choose PaaS for 3 main reasons; First, PaaS platforms are easy to deploy due to their build-in features for development and scalability. Second, PaaS pricing is usage-based, therefore it is easy to control the cost. Third, Identity and access management features can provide robust security.
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Thank you for sharing. These are some additional preventive measures I think that can be considered to address the challenges of using AI chatbots.
> We can make sure that third-party vendors, including chatbot providers like OpenAI, sign business associate agreements (BAAs) that clearly state their responsibilities for safeguarding patient data and complying with HIPAA regulations.
> We can encourage responsible and ethical handling of patient information by healthcare providers, and also implement laws and regulations that can hold healthcare providers accountable for their use of AI chatbots and the data they upload.
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Thank you for sharing the case study. In addition to what you’ve discussed, I’d like to provide some additional measures for prevention:
> Incident Response Plan: Develop and regularly update an incident response plan that outlines the steps to be taken in case of a data breach. Ensure that the plan includes clear communication strategies, responsibilities, and procedures for notifying affected individuals promptly.
> Regular Security Audits of Third Parties: In addition to vendor assessments, conduct regular security audits of third-party vendors to ensure that they are continuously meeting security standards and promptly addressing vulnerabilities.
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The preventive measures discussed in response to the data breach at Yakima Valley Memorial Hospital are comprehensive and aligned with best practices in healthcare data security. However, I’d like to provide some additional insights and suggestions:
> Real-Time Monitoring and Alerts: Consider implementing real-time monitoring solutions that not only detect unusual access patterns but also trigger immediate alerts when unauthorized access is detected. This can enable rapid response to security incidents.
> Regular Security Drills and Simulations: Conduct regular security drills and simulations to train employees on how to respond to security incidents effectively. This can help in preparedness and minimize the impact of breaches.
> Collaboration and Sharing of Threat Intelligence: Establish channels for sharing threat intelligence and best practices with other healthcare organizations to collectively strengthen cybersecurity defenses.
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I had experience using a contact tracing app called “Taiwan Social Distancing App” to help manage and control the spread of COVID-19. This app was developed by the Taiwanese government and was used to track individuals’ movements and monitor their quarantine status during the pandemic using GPS and Bluetooth tracking.
There were some limitations of this app:
1. Privacy Concerns: It has faced privacy concerns, with users hesitant to share personal information due to potential misuse or data breaches.2. Limited User Engagement: User adoption rates for contact tracing apps have been uneven, partly due to user interface issues and a lack of engagement strategies. Since the app was only availabe in Chinese, it was difficult for foreign residents to widely use it. It also received several complaints in Playstore for not being able to use in some phone models and brands.
Knowledge and Skills of Health Informatics to Improve the Project:
1. Ethical Considerations: Health informatics experts can help identify and address ethical dilemmas related to data collection, storage, and usage, building public trust in the app.
2. User-Centered Design: Health informatics professionals can apply user-centered design principles to create a more user-friendly interface, improving user engagement and overall usability.
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I have various experiences in public health from data management to programmatic management. For me to improve my skills as a health informatician, I would need to focus on gaining a deep understanding of healthcare systems, epidemiology, and biostatistics. I would also need to develop expertise in data governance, compliance, and analytics tools, and stay updated on healthcare technologies and standards.
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Hello everyone. I am Noi Yar from Myanmar. I am a medical doctor currently working as a research assistant part-time at Shin Kong Wu-Ho Su Memorial Hospital, Taiwan. I had a chance to participate in some mobile health implementation and evaluation projects after graduation and gained an interest in health informatics subject. I believe this program will equip me with the necessary knowledge and skills to thrive in this field. Glad to meet you all.
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I couldn’t agree more about the importance of learning social science and change management. As we are moving towards wide use of electronic medical record systems in Myanmar in recent years, it’s crucial for a smooth transition from paper records to electronic systems. A lot of clinicians in Myanmar are still very resistant to the use of electronic medical records for multiple reasons particularly due to unfamiliarity with technology and already workload.
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