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As a Clinical Data Management adhering to the CIA Triad (Confidentiality, Integrity, and Availability) is critical in managing sensitive clinical trial data. While I don’t have personal experiences, I can share an illustrative scenario based on common challenges in clinical data management:
Incident: Breach of Confidentiality
What happened?
An email containing participant IDs and sensitive clinical data was inadvertently sent to an unauthorized recipient due to human error. The email was not encrypted, increasing the risk of data exposure.How did it affect the system or users?
• Compromised participant privacy, potentially violating regulatory requirements like HIPAA.
• Erosion of trust with trial participants and stakeholders.How to prevent it?
• Implement secure communication protocols: Use email encryption tools and ensure sensitive data is shared only through secure platforms.
• Training and awareness: Conduct regular training sessions for staff on data handling and security protocols.
• Role-based access control: Ensure only authorized personnel can access sensitive information. -
In my role as a Clinical Data Manager, recalling details accurately during project meetings is crucial. One instance where I found this skill lacking was during a project meeting with the clinical trial team. We were discussing the latest data validation issues, and there were several points raised about discrepancies in participant data entries. However, I was distracted by the sheer volume of information being shared and struggled to recall the specific details of the different issues discussed.
When I was asked to follow up after the meeting, I realized I couldn’t recall some of the finer points, such as which data fields were causing the most trouble or the specific actions needed from the team to resolve the discrepancies. As a result, I had to spend additional time going back through meeting notes and asking colleagues for clarification. This delay affected the project timeline and added unnecessary back-and-forth communication.
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I consider empathy my strongest Emotional Intelligence component. In managing clinical data, I frequently interact with diverse teams and study participants, each with unique perspectives and challenges. My empathy enables me to understand others’ emotions and motivations, fostering trust and rapport. For instance, I acknowledge the efforts of research personnel working with limited resources, which promotes camaraderie and open communication—ultimately enhancing teamwork and outcomes.
Conversely, motivation is sometimes challenging; I occasionally struggle to maintain intrinsic drive in lengthy, repetitive projects where results are delayed. Extended timelines and stringent protocols can impact my engagement, affecting productivity in certain tasks.
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1) Should you give the data out?
Individual-level data should not be disseminated. It contains sensitive personal information that can be used to identify patients, thereby violating their privacy rights and ethical standards without their explicit consent. The sharing of such data may also violate legal obligations under laws such as GDPR or HIPAA, thereby eroding the trust between healthcare providers and vulnerable populations.2) How do you not violate Informatics Ethics?
To uphold ethical principles, ensure informed consent is obtained before using personal data. Actions should aim to benefit the community without causing harm. Access to health data should be equitable, and transparency in data usage policies must be maintained.3) If you want to provide the data, what steps should you take?
Assess the Request: Evaluate the research team’s objectives to ensure they align with ethical standards and provide benefits to the local population.
Encourage Aggregate Data Usage: Suggest using aggregate data for valuable insights while protecting individual privacy.
Data Anonymization: If individual data is necessary, anonymize it by removing identifiable information and implementing re-identification prevention methods.
Formal Data Sharing Agreement: Create an agreement detailing data usage, sharing restrictions, responsibilities, and data protection measures.
Ethics Review: Submit the proposal to an ethics committee for evaluation.
Community Engagement: Inform stakeholders about the research and its benefits to build trust.
Data Security Measures: Establish protocols for data sharing and access.
Monitoring and Evaluation: Monitor data usage and conduct a post-study evaluation for compliance and community impact assessment.
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I encountered a multifaceted ethical challenge as a health information worker in this scenario. However, I shall address the matter while adhering to the concept of Ethics and confidentiality.
1) What should you do?
Even though I really wanted to tell my friend But I should put patient confidentiality first and not provide any details about my friend’s husband’s HIV status. Pay attention to my professional responsibilities and make sure that ethical and privacy regulations are followed.2) As a health information professional – can you tell my friend?
I had to force myself to keep this information a secret from my friend. As a health information expert, I can’t tell a friend about her husband’s HIV status. Such actions are a breach of confidentiality and violate ethical guidelines.3) Can you interfere with other people or family issues?
Intervening in personal or familial affairs, especially with sensitive health information, is typically inappropriate. My responsibility is to manage health information judiciously.4) But, should your friend not know about this because she might be at risk?
Although my concern for my friend’s health is valid, the choice to disclose this information ultimately lies with the patient (her husband). It is essential to respect his autonomy and his right to decide when and how to share his health status.5) How will you follow the fundamental principles about the right to self-determination, doing good, and doing no harm to others?
• Respecting the husband’s choice regarding his health information.
• Encouraging the husband to consider seeking support or counseling to discuss his condition with his wife if he feels comfortable.
• Avoiding actions that could harm the husband or disrupt his relationship by disclosing his condition without his consent.6) Isn’t it your obligation and the right of the subject to hold the information?
Yes, it is both my obligation as a health information professional to protect the confidentiality of the patient and the right of the patient to control access to their health information. Upholding these rights is fundamental to maintaining trust and ethical standards in healthcare. -
Following the effective implementation of SAP Business One (Enterprise Resource Planning (ERP) solution) inside the pharmaceutical company. In my opinion, the process of change was well managed. I will explain how they manage the change using the ADKAR model as follows;
Awareness: The organization effectively communicated the need for SAP Business One, highlighting how it would improve processes and enhance efficiency within pharmaceutical operations.
Desire: Leadership likely fostered a positive attitude toward the change, engaging employees by addressing their concerns and illustrating the benefits of the new system, thereby creating a desire to support the implementation.
Knowledge: Comprehensive training and resources were provided, ensuring that employees understood how to operate SAP Business One and its features. This knowledge transfer was crucial for effective adoption.
Ability: Employees were likely supported in applying their training, with hands-on practice and access to ongoing support, enabling them to use the system confidently in their daily tasks.
Reinforcement: Post-implementation, the organization probably implemented mechanisms to reinforce the use of SAP Business One, such as ongoing training sessions, performance metrics, and recognition of employees who excelled in utilizing the new system.
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I have worked in a pharmaceutical company that successfully implemented SAP Business One. SAP Business One is an Enterprise Resource Planning (ERP) solution. It helps businesses manage key operations, including financials, supply chain, inventory, production, sales, and compliance. For pharmaceutical companies, SAP Business One provides a tailored solution that meets the complex regulatory and operational needs of the industry while being more cost-effective and scalable than larger ERP systems like SAP S/4HANA.
For the successful implementation of SAP Business One in a pharmaceutical company, several key factors across Data, Cost, Operation, Design, and People are essential:
Data: High-quality, accurate data is crucial for batch tracking and compliance reporting. Seamless integration across departments ensures real-time access to up-to-date information, while robust security measures maintain compliance with regulatory standards.
Cost: The system offers an affordable solution for small to medium-sized companies, requiring careful budgeting for licenses, customization, and maintenance. Effective management of the total cost of ownership and a focus on return on investment (ROI) through efficiency improvements are vital.
Operation: Automation streamlines inventory control and production workflows, reducing human error and ensuring consistent product quality. Real-time batch management is necessary for compliance with industry standards and regulatory requirements.
Design: Customization to meet pharmaceutical needs, scalability for growth, and a user-friendly interface are critical for effective adoption and operational efficiency.
People: Strong leadership and commitment are essential for driving the project, along with a clear change management strategy and thorough training. Cross-departmental collaboration ensures the system aligns with all business processes and supports the entire supply chain.
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In clinical data management, like the one I work with, a Decision Support System (DSS) in clinical research is a technology-driven tool designed to assist researchers and clinicians in making informed decisions regarding study design, patient management, data collection, and analysis. These systems leverage data, algorithms, and clinical guidelines to enhance the decision-making process, ultimately leading to improved patient outcomes and more efficient clinical trials.
Factors that might influence the implementation of a Decision Support System (DSS) in the clinical research field include:
Compatibility: The DSS must seamlessly integrate with existing systems, such as Electronic Health Records (EHRs), Clinical Trial Management Systems (CTMS), and laboratory information systems. Incompatibility can hinder data flow and usability.
Interoperability: The ability of the DSS to communicate across different platforms and databases is crucial, especially in multi-site trials.
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If hospitals in a country do not use the ICD standard, it would create barriers in patient care, research, and public health efforts. It could compromise health data quality, impede efficient communication, complicate health insurance processes, and isolate the country from global health initiatives. Using ICD ensures consistency, clarity, and interoperability in healthcare systems, which are vital for efficient healthcare management:
Inconsistent Diagnosis Coding: Without a unified system like ICD, hospitals may develop or use various classification methods, leading to inconsistencies in recording diagnoses across different institutions. This would make it difficult to compare medical data and outcomes across facilities.
Confusion in Patient Care: The absence of a standardized coding system could lead to misunderstandings or misinterpretations of diagnoses, particularly if patients move between hospitals, affecting continuity of care.
Inefficient Health Information Exchange: ICD allows for seamless data sharing between healthcare providers, insurers, and health authorities. Without it, sharing patient records and health information would be more complex, and electronic health record (EHR) systems may struggle to communicate with each other.
Impaired Disease Tracking and Surveillance: ICD plays a crucial role in tracking the incidence and prevalence of diseases for public health purposes. Without a standardized classification, health authorities might find it difficult to monitor and respond to disease outbreaks or public health trends.
Challenges in Medical Research: Researchers rely on standardized data to study diseases, treatments, and outcomes. A lack of standardization could lead to unreliable or incomparable datasets, impeding medical research and evidence-based healthcare improvements.
Limited Participation in International Health Initiatives: The use of a non-standard system could isolate the country from international research collaborations, initiatives, and health programs that rely on ICD-coded data.
Higher Administrative Costs: Hospitals might need to create multiple mapping systems between different classification methods, increasing administrative complexity and costs.
Inconsistent Global Health Metrics: ICD is used globally by the World Health Organization (WHO) to report and compare health statistics. Without it, the country may face challenges in contributing to or analyzing global health metrics, making it difficult to benchmark against other nations.
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What do you think about this finding? Have you ever heard any complaints from health officers (or yourself) on using EMR?
The findings in the document highlight significant challenges that Electronic Health Records (EHRs) pose to healthcare professionals, leading to physician burnout. This resonates with broader concerns, the causes of burnout identified, such as the overwhelming amount of time spent on documentation, poorly designed user interfaces that complicate workflows, are all issues I’ve heard echoed by health officers.
Any suggestions to avoid or reduce this problem.
To address or reduce this problem, I would suggest the following strategies:• Improved EHR Design: Simplifying the interface, reducing unnecessary steps, and making it more intuitive could ease the burden on healthcare workers.
• Task Delegation: Assigning non-clinical EHR tasks to support staff can free up physicians to focus more on direct patient care.
• Training and Ongoing Support: Offering comprehensive training on EHR usage and continued support could reduce the learning curve and alleviate some of the stress.
• Feedback Mechanisms: Regularly gathering and acting on feedback from users can lead to system improvements that are aligned with actual workflow needs. -
Hello Tanaphum, Your presentation on the MorDee App highlights an incredibly practical and forward-thinking telemedicine platform. The ability for users to consult with doctors across various specialties through multiple channels—video call, chat, or phone—makes healthcare accessible and convenient. I love how the app not only simplifies scheduling and finding doctors based on symptoms but also includes the added benefit of medication delivery. The integration with insurance for seamless treatment claims without upfront payments is a huge plus, making the app even more user-friendly. Overall, this is an excellent solution to modern healthcare challenges—well done! 🙂
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Hello Aung Thura Htoo, First of all, I want to commend you on your various works. I had the chance to see your work in the Data Virtualization course, and it was amazing! Your work has truly inspired me to aim for higher standards in my own projects. 🙂
Your presentation on the eHealth project sounds impressive! Highlighting ELSA, a mobile application co-developed by patients, healthcare professionals, and researchers, demonstrates a great collaborative effort. The focus on empowering individuals with rheumatic diseases to better manage their condition while fostering connections between patients and healthcare providers is both innovative and patient-centered. I’m sure your audience will appreciate the clarity and importance of your message. Great job on creating a well-rounded and impactful presentation!
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Shaw et al (2017), identify three overlapping domains of eHealth: Health in Our Hands (using eHealth technologies to monitor, track, and inform health), Interacting for Health (enabling health communication among practitioners and between health professionals and patients), and Data Enabling Health (collecting, managing, and using health data). These domains form a comprehensive model that captures the complexity and potential of eHealth, providing a framework for its operationalization in various health contexts.
In my opinion, the definition of eHealth is the application of digital technology for managing health data, facilitating stakeholder communication, and tracking, monitoring, and informing health. It includes a diverse range of instruments and methodologies designed to optimize healthcare delivery, augment patient participation, and assist health professionals.
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Healthcare Data from Wearable Devices: Wearable health technology (like fitness trackers and smartwatches) collects extensive data about users’ health metrics, such as heart rate, activity levels, sleep patterns, and more.
Big Data Characteristics (5Vs, 7Vs, 10Vs)
1. Volume: Millions of users generate terabytes of data daily from their devices. The sheer scale of data collected from numerous devices across a large population qualifies as Big Data.
2. Velocity: Continuous data streaming from wearables in real-time, such as heart rate monitoring or step counts. Data is generated at high speed, requiring real-time processing to provide immediate insights (like alerting users to abnormal heart rates).
3. Variety: Data includes structured data (like heart rate and steps), unstructured data (like user-generated notes or logs), and semi-structured data. Wearable devices collect a diverse array of data types, making the dataset rich and complex.
4. Veracity: Data may vary in accuracy depending on device calibration, user behavior, and context (e.g., heart rate readings during exercise vs. rest). Ensuring data quality is crucial because inaccurate health data can lead to incorrect health assessments or alerts.
5. Value: Insights derived from the data can help in personalized health recommendations, predicting health risks, and improving overall wellness. The ability to analyze user health data provides significant value to both users and healthcare providers for proactive health management.
6. Variability: Data usage can spike during certain events (like fitness challenges) or vary based on user engagement (e.g., a user may only wear the device sporadically). The variability in data flow based on individual usage patterns makes it essential to adapt analytics accordingly.
7. Visualization: Dashboards that visualize heart rate trends, activity levels, and sleep quality over time. Effective visualization tools help users and healthcare professionals interpret complex data easily and make informed decisions.
8. Volatility: Real-time alerts (like an abnormal heart rate) are only relevant for a short period, while long-term trends in fitness may be analyzed over the years. Some data is transient and needs immediate action, while other data contributes to long-term health assessments.
9. Validity: Ensuring that data collected (like blood pressure readings) is accurate and aligns with clinical standards. Validity is crucial to ensure that health data can be trusted for making health decisions.
10. Vulnerability: Sensitive personal health data collected by wearables must be protected against unauthorized access and breaches. The sensitive nature of health data requires stringent security measures to protect user privacy and comply with regulations like HIPAA.
The data generated by wearable health devices has significant volume, is produced at high velocity, contains diverse variety, faces challenges in veracity, and has high value for health insights. The additional variability, the need for effective visualization, and concerns around volatility, validity, and vulnerability further illustrate its complexity as Big Data.
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Have you ever observed a health informatics project in your (other) organization? Please provide a brief introduction.
I work in the field of Clinical research, and I have participated in a health informatics project that related to Clinical Trials using paper-based data collection methods. In the field of clinical research, efficient and accurate data collection is crucial for assessing the safety and efficacy of treatments.
Patients’ clinical data, including demographic information, medical history, and treatment outcomes, will be recorded using structured case report forms (CRFs). These paper forms will serve as the primary means of collecting data during patient visits. To maintain data quality, trained research personnel will ensure the accuracy and completeness of the forms, which will be validated through regular quality checks.
However, Using paper-based data collection in clinical trials introduces several risks that can impact the quality, accuracy, and efficiency of the trial. Below are the key risks associated;
• Data Entry Errors: Manual data entry into paper forms increases the likelihood of human errors, such as illegible handwriting, transcription mistakes, or missing data.
• Data Loss or Damage: Paper forms are susceptible to physical damage or loss due to poor storage practices, natural disasters, or mishandling.
• Limited Data Security: Paper records are harder to secure compared to electronic systems. Unauthorized access, theft, or breaches of confidentiality can occur if paper forms are not properly stored.
• Time-consuming and Labor-Intensive
• Difficulty in Real-Time Data Monitoring
• Increased Risk of Data Duplication or Inconsistencies
• Challenges with Regulatory Compliance: Paper records can make it difficult to demonstrate adherence to regulatory requirements, such as audit trails, timestamps, and data integrity standards required by agencies like the FDA.
• Environmental and Operational Costs: Paper-based data collection involves significant use of physical resources, such as paper and storage space, which increases the environmental footprint and operational costs of the trial.How can this health informatics project help to improve the current practices?
Implementing Electronic Data Capture (EDC) systems for clinical trials significantly improves many aspects of traditional paper-based clinical trials. Below are the key benefits of transitioning to an EDC system from a paper-based approach:
• Real-time Data Entry: In EDC systems, clinical data is entered directly into the system by study personnel, reducing transcription errors that are common when transferring information from paper to digital format.
• Automatic Data Validation: EDC systems allow real-time validation checks, such as range checks, consistency checks, and alerts for missing or incorrect data. This improves data accuracy and minimizes the need for manual review later.
• Audit Trails: EDC systems automatically track changes to the data, providing an audit trail for regulatory compliance and quality control.
• Faster Access and Monitoring: EDC systems allow for real-time data access for sponsors, clinical research organizations (CROs), and study teams. This speeds up the monitoring process, making it easier to track the trial’s progress.
• Reduced Paperwork: EDC systems eliminate the need for physical paper forms and storage, simplifying the organization of large volumes of data. This makes the data management process more streamlined and reduces the risk of lost or damaged records.
• Centralized Data Repository: All trial data is stored in a centralized, secure digital repository, making it easier to manage, retrieve, and analyze information. This also facilitates data sharing across study sites.
• Regulatory Compliance: EDC systems are designed to comply with regulatory standards such as 21 CFR Part 11 (in the US), ensuring the integrity and security of clinical trial data. The system’s built-in audit trails also help meet the regulatory requirements of various authorities.Are there any challenges or difficulties in implementing the project?
While EDC systems provide numerous advantages over paper-based trials, there are also challenges:
• Cost of Implementation: Initial setup costs for EDC systems, including software, hardware, and training, can be high, especially for smaller organizations or trials in resource-limited settings.
• Learning Curve: Training study personnel, investigators, and site staff to use the EDC system effectively may take time and effort, especially if they are more familiar with paper-based systems.
• Technical Issues: Data security, system downtime, and technical support are essential considerations. A well-maintained system with sufficient IT support is required to ensure smooth operation.
• Internet Access and Infrastructure: In resource-limited settings, reliable internet access and technical infrastructure can be a barrier to fully implementing EDC systems. -
Hello Wannisa, Thank you for sharing your report. It presents valuable suggestions for preventing attacks. It may be beneficial to establish a robust incident response plan that includes specific protocols for handling data breaches, communication strategies, and coordination with legal and regulatory bodies. Regularly updating and testing the plan can ensure preparedness for any future incidents.
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Hello Tanaphum, your report provides helpful suggestions for preventing attacks and brief information about the Power Diary attack. To prevent unauthorized parties from sending emails that appear to originate from your site, it may be helpful to implement comprehensive logging and monitoring of system activity. This can assist in the real-time detection of suspicious behavior and the prompt response to any potential breaches.
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Hello Alex, your report presents valuable suggestions for preventing attacks. It could be further strengthened by recommending the implementation of Multi-Factor Authentication (MFA) to enforce its use when accessing sensitive data and systems, thereby adding a layer of security. Additionally, proposing data segmentation would help limit the sharing of sensitive information with third-party vendors and minimize the potential impact of a breach.
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1. Why Choose a Cloud Server Instead of a Physical Server?
The proposal recommends adopting a cloud server over a physical server for developing a web-based patient appointment system due to the following benefits:
Cost Efficiency: Cloud services are more cost-effective with a pay-as-you-go model, avoiding large upfront investments required for physical servers.
Scalability: Cloud servers can easily scale with patient volume and hospital growth, unlike fixed-capacity physical servers.Availability and Reliability: Cloud providers offer high uptime guarantees and automatic failover, ensuring continuous service availability.
Maintenance: Outsourcing server maintenance to a cloud provider reduces the workload on the hospital’s limited IT staff.Data Backup and Recovery: Cloud services include automated backup and disaster recovery, protecting patient data.
Security: Cloud providers implement advanced security measures and comply with healthcare regulations, ensuring data protection.
Faster Deployment: Cloud servers can be set up quickly, allowing for efficient and timely deployment of the appointment system.
2. What Cloud Computing Service Model Would Be Most Appropriate?
The most appropriate cloud computing service model for this project would likely be Platform as a Service (PaaS). Platform as a Service (PaaS) is the most suitable cloud computing service model for developing the custom web-based patient appointment application due to the following reasons:
Tools and environments for easy development, testing, and deployment.
Integrated features such as databases, security, and scalability streamline the development process.
Reduced management requirements for IT staff, allowing them to focus on critical tasks.Healthcare-specific security and compliance features.
Greater cost-effectiveness compared to Infrastructure as a Service (IaaS) due to less manual configuration and ready-to-use solutions.
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1. Phishing Attacks
Links to Fake Websites: The message usually contains links to websites that closely resemble legitimate sites. When the victim enters their credentials or personal information on these sites, it is captured by the attacker.
Malicious Attachments: Some phishing emails include attachments disguised as legitimate documents. When opened, these attachments can install malware on the victim’s computer.2. Malware Infections
Rootkits: A set of tools that allow attackers to gain administrator-level access to a system while hiding their presence.
Spyware: Malware that secretly monitors user activity and collects personal or sensitive information without consent.3. SQL Injection
SQL Injection (SQLi): where an attacker exploits vulnerabilities in a web application’s database query process by injecting malicious SQL code into input fields or URLs. This attack allows the attacker to manipulate the database, potentially gaining unauthorized access to sensitive data, altering records, or even taking control of the entire database.4. Password Cracking
Dictionary Attack: Now, suppose the user’s password is “password123,” a relatively weak and common password. The attacker uses a dictionary attack, which tries commonly used passwords from a list (called a dictionary). The dictionary file contains common passwords such as: “123456” OR “password” OR “password123”. Since “password123” is a common password, the attacker finds it quickly in the dictionary list, which speeds up the process compared to brute force. -
An example of an existing Health IT project is COVID-19 Contact Tracing Apps.
COVID Alert (Canada): An app developed to notify users if they have been in close contact with someone who has tested positive for COVID-19. (Reference: COVID Alert: Canada’s exposure notification app – Canada.ca)
Problems or Limitations of the Project:
1) Privacy Concerns: Many users hesitate to use contact tracing apps due to concerns about data collection, usage, and sharing. There are fears about surveillance and misuse of personal information.
2) Data Integration: Integrating data from various sources, such as public health records and test results, can be challenging. This may result in gaps in data and inefficiencies in tracing and notification processes.
3) User Adoption and Engagement: The effectiveness of these apps relies heavily on high user adoption rates. Low download or inconsistent use can diminish the app’s effectiveness in contact tracing.
4) Technical Limitations: Issues such as false positives or negatives and inaccuracies in Bluetooth-based proximity measurements can affect the app’s ability to detect potential exposures accurately.Health Informatics Knowledge and Skills to Improve the Project:
1) Data Privacy and Security: Expertise in health informatics can enhance the design and implementation of robust privacy safeguards and encryption methods to protect user data. This includes developing clear privacy policies and ensuring transparency about data usage.
2) Data Integration and Interoperability: Knowledge of health information systems and standards (e.g., HL7, FHIR) can improve the integration of data from various sources, allowing the app to effectively combine information from different health records and testing sites.
3) User Engagement Strategies: Skills in health communication and behavioral science can help design user-friendly interfaces and engagement strategies that promote higher adoption and consistent use of the app. This includes understanding barriers to adoption and addressing them through targeted communication and incentives.
4) Evaluation and Optimization: Applying skills in data analysis and epidemiology can aid in evaluating the app’s effectiveness and identifying areas for improvement. This involves analyzing data to understand patterns and effectiveness and optimizing algorithms for more accurate contact tracing.
5) Public Health Expertise: Incorporating knowledge of public health principles and outbreak management can enhance the app’s functionalities to better support public health responses. This includes understanding the epidemiological aspects of contact tracing and integrating the app’s data with broader public health strategies. -
Based on my background as a data manager (DM) and Clinical Research Associate (CRA) in the clinical research field, I would need to gain the following knowledge and skills to further improve my profession in public health or health informatics:
1) Health Data Standards and Interoperability:
Knowledge: Learn about health data standards which are essential for sharing and integrating health data across systems.
Skills: Gain the ability to ensure data integrity, quality, and seamless interoperability between various health informatics systems.
2) Epidemiology and Biostatistics:
Knowledge: Understanding core public health concepts such as epidemiological research methods, biostatistics, and health outcomes assessment will enhance my ability to design, analyze, and interpret health data.
Skills: Develop expertise in performing statistical analyses using tools like SAS, STATA, or R to assess population health trends and the impact of interventions.
3) Data Analytics and Machine Learning:
Knowledge: Learn advanced data analytics methods, including machine learning, artificial intelligence, and big data analytics, which are increasingly used in public health for predictive modeling and decision-making.
Skills: Familiarize myself with tools like Python or R for implementing machine learning models and applying them to clinical research and public health data.
4) Data Privacy and Ethics:
Legal Frameworks: Strengthening knowledge of data privacy regulations and ethical principles in health data management to ensure compliance and protect patient confidentiality.
5) Data Visualization and Communication:
Knowledge: Enhance my skills in translating complex data into actionable insights for stakeholders.
Skills: Master tools such as Looker Studio, Tableau, or Power BI to create intuitive visualizations and reports for clinical, public health, and policy decision-making.
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Hi, everyone.
Your dashboard looks great! Thank you for sharing 😊Here is the link to my dashboard for the final project assignment: https://lookerstudio.google.com/s/ryRhrqBRS9E
This COVID-19 surveillance dashboard offers detailed insights into the state of the pandemic during 2020-2022. I’ll examine key metrics such as confirmed, recovered, and death cases. The data can be sorted by continent, country, or specific periods using the dropdown buttons. Furthermore, you can get the report by selecting the ‘Download Report’ button.
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Thank you for sharing the data visualization dashboard for COVID-19 disease. I would like to share my selected COVID-19 dashboard from the Ministry of Health Malaysia COVID-19 · https://data.moh.gov.my/dashboard/covid-19/jhr. (The last update data as of 01 Jun 2024, 23:59). I would like to describe what I like and dislike about this dashboard.
What I like:
• The dashboard layout is clean.
• Design the dashboard with the end user in mind, Make it easy for users to navigate and interact with the dashboard by providing intuitive controls and clear instructions.
• Maintain consistency in visual elements such as colors, fonts, and iconography throughout the dashboard to create a cohesive. Consistency helps users quickly understand the dashboard’s structure and meaning.
• Interactive Features: Incorporate interactive elements such as filters, dropdown menus, and drill-down capabilities to enable users to explore the data and customize their views. Interactivity enhances user engagement and empowers users to derive deeper insights from the data
• Appropriate chart types, labeling axes clearlyWhat I dislike
• To focus on presenting the most important information prominently and avoid unnecessary visual elements that may distract from the key insights.
• The dashboard is not responsive and adaptable to different screen sizes and devices (i.e. tablets and smartphones) -
Hello, Teerawat. I appreciate that I have been assigned to review your CRF. Your CRF appears to be very impressive. 😊 Please find my following comments:
– I would suggest relocating the ‘Informed consent process’ to the first section of the CRF, preceding the ‘Demographics’ section. This is because the ICF process should be carried out as the initial step in every procedure.
– Regarding the eligibility criteria section on Page 3, I recommend either removing the “N/A” (Not applicable) option for Inclusion criteria No. 1-3, or replacing it with “Not done.” The “N/A” option should only be kept for Inclusion No. 4, in the case of male participants. To implement exclusion no. 2, kindly include a ‘N/A’ option for male participants.
– In the Laboratory Test (Female Only) section, I suggested replacing the ‘Serum pregnancy test’ with the ‘urine pregnancy test’ based on the study synopsis.
– The CRF page for ‘subject disposition visit’ is not available. If the subject is not eligible for the study, I would suggest including a specific box for the reason for ineligibility can be entered, such as full enrollment or meeting exclusion criteria.
– In regards to the Informed Consent Process (Page 4): Kindly include a box in which the time of the Informed Consent Form (ICF) is provided.
– Vital signs section (Page 5): The study description does not include precise information about the route of temperature measurement for the ‘Temperature’ variable. I recommend changing ‘Oral Temperature’ to ‘Body temperature’ and including the subsection ‘Route of temperature measurement’ below ‘Body temperature’. The options for selecting should be ‘Oral’, ‘Forehead’, ‘Axillary’, or ‘Other’ with the box to specify. Next, the ‘Not done’ checkbox should be added for each Vital Sign.
– PE (Page 6): I recommend including the ‘Time of assessment for PE’. The Vital signs and Physical Examination (PE) are recorded on a separate Case Report Form (CRF) page. Therefore, it may be ensured that the physical education (PE) time is conducted after the ICF is signed. I would like to include an additional category called ‘Other categories’ below the ‘Metabolic/Endocrine’ row to record additional PE systems.
– I would suggest modifying the section titled ‘Eligibility check’ to ‘Subject history’ or ‘Medical history’ instead.
– Subject Check (Page 7): As per the sentence ‘If this is answered NO, please clarify’ in this section, I suggest adding a specific box for the reason for ineligibility that can be entered.
– Laboratory Test (Page 8): The study synopsis states ‘Immunogenicity will be evaluated using the hemagglutination inhibition (HAI) assay to measure influenza antibody level (reported as HAI titer)’. I would suggest removing the part on ‘Safety Labs – Blood Sample Collection’ from this page.
– Specialty Labs – Blood Sample Collection (Page 7): Please add a text box for entering the ‘HAI titer’ for each category of HAI.
– Randomization Process (Page 9): I greatly appreciate this topic you have created since it is an excellent tool for generating numbers related to the subject. Just to update the “Serum pregnancy test” to the “Urine pregnancy test” following the study synopsis.
That’s great!
Siriluk -
I would like to improve the ‘Physical examination’ section in the following:
Specify the unit ‘kg’ for weight, ‘cm’ for height, as well as the unit ‘mmHg’ for blood pressure, with separate values for ‘Systolic’ and ‘Diastolic’. In addition, provides a ‘Body system’ such as HEENT, Cardiovascular, Chest, Abdomen, Musculoskeletal, and Neurological. Furthermore, it would be advantageous to add the box to select an ‘Other system’ if required. The Physical Examination (PE) part can design the template as a table with checkboxes for each PE system, indicating as normal, abnormal, or not done.
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Yes, I have experience managing data in a software program called DFexplore. The data will be stored and managed in the clinical data management system. DFexplore is a standalone application. It does not need to run on any specific web browser. Since my company provides data management services, we must complete all these steps before providing it to our clients. So, we implemented several data management processes within DFexplore to ensure data quality and integrity:
Audit trail/Time stamp: All collected data and actions (data entry, modification, and deletion) in the system can be tracked using an audit trail that includes date, time, and username will be shown in the audit trail report.
User authentication and access control level: Access is only granted to qualified personnel as requested by the sponsor and/or investigator of the study. The user will have access to only study and application features that the study administrators have granted permission to use. The level of access control will be defined per each user’s roles and responsibilities (e.g., data entry, study coordinator, principal investigator, or monitor). Each person responsible for entering or reviewing the study data in the eCRF will receive a unique username and a temporary password from study administrators (DM team).
Edit check and logical check: The DM team will implement the edit check (EC) program into the study database to check missing data/consistency of the database during/after data entry based on “Edit Check Specifications (ECS)”, while the user is entering the study data into the eCRF on the server, the edit check program will make certain checks and may flag that the data needs to be reviewed, confirmed, or revised. This process of automated edit checks will be implemented to reduce errors in the data entry process.
Data backup and recovery plan: We will maintain the database by allocating sufficient system storage for the study database and upgrading database software and associated applications, if necessary. Providing routine backup of the database storage and performing routine virus checks on incoming and outgoing data. The backups will be performed on a daily basis for their ability to adequately restore the backed-up data on the backup server.
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Based on my experiences, I have worked as a Clinical Research Associate (CRA) in the clinical vaccine trial field for a pharmaceutical company. I have participated in steps of the workflow in Protocol Discussion and Data Design (Variables and Data Work Flow). The pharmaceutical company I work for serves as the “sponsor” for the research. So, I assisted the clinical team in all activities related to clinical trial preparation, including protocol discussion and implementation.
After that, I joined the Data Management Service company, and I work as a Data Manager (DM). The Data Management Service is responsible for all steps in the data management process workflow. In my role, I participated in project initiation and during study conduct as follows:
– Data Collection/Case Report Form (CRF) Development: I created and maintained the Case Report Form (CRF).
– Development of a Data Management Plan (DMP): My task involved providing input and reviewing the data management plan.
– Database Access Control: I am responsible for managing the database’s access control.
– Database Setup and Edit Checks Programming/Data Entry Screen Test: My task involved setting up the database and conducting programming and data entry screen tests. The process also includes performing database testing, specifically data entry screen tests, and preparing an edit check specification for the programmer team to test before the database launches.
– Investigator Meeting/CRF Completion Training: My task involved providing training to the study team and coordinating with the study team or sponsor for overall project management aspects.
– Data Validation/Data Quality Control (QC): Validate study data in the clinical data management system and ensure database accuracy during the course of clinical studies as well as coordinate with the study team and sponsor to address study data-related issues.
– Data entry and processing: Prepared CRF completion guidelines and sent them to the study site.I would like to improve the edit check program so that it can be used in full function to support the process of data validation if the research uses an EDC system. Currently, edit-check programs are a feature that can help with data cleaning, data validation, and QA processes. However, it still takes DM staff a significant amount of time to complete this specific step, which ensures the data is clean and ready for analysis. Therefore, developing an edit-check program for full function or creating another tool could potentially reduce the time staff spends performing these steps, while also ensuring high-quality data for the data analysis process.
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This is the summary what I’ve learned for this week.
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1. Purpose of data collection: For research, for public health surveillance, or others.
The research project I am involved in is a vaccine trial that will assess the immunogenicity and safety of the Vaccine in healthy adults.
2. Was it primary or secondary data collection?
Both types of data collection were performed.
– Hospital data collection form
– Medical records/ Medical charts
– Laboratory results
– Interviews
– oral histories
– Diary card3. Methods used for data collection
– EDC (Electronic Data Capture) system: Used to collect, store, and manage research data electronically in a secure.
– ePRO (electronic Patient-Reported Outcomes) mobile application: Used to collect patient-reported outcomes (Local and systemic reactogenicity) during the conduct of clinical trials.4. Were there any problems that occurred regarding data collection?
– Data Quality: It can come from a variety of sources, including data entry errors or datasets that may have missing, inconsistent, or invalid values that reduce reliability. This can be due to human error, technological failures, or flaws in collection methods.
– Complicated forms can lead to nonresponse or respondents filling in answers at random simply to complete the survey.
– Literacy comprehension barriers.
– Language comprehension barrier.
– Data Integration: Internal systems, third-party applications, and external data sources.
– Insufficiently trained staff.
– Lack of understanding of context.
– Data inaccuracy – Even complete datasets can contain incorrect values due to human data entry mistakes, respondent misreporting, technology errors, flaws in measurement methods, and more.
– Data collected may have compatibility issues around file formats, metadata standards, and barriers to consolidation. -
This is what I have learned this week. (WEEK 3: CYBER HEALTH CARE)
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Hi everyone, this is my summary infographic for week 2.
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Hi everyone, my summary of what I have learned about this topic.
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Hello Aye Thinzar Oo, thank you for watching and replying to my VDO 🙂
Please see my response below,DFengage: Works Online and Offline
Online
► When online, once user clicks “Finish,” data for that activity is sent to DFdiscover via API and removed from the app.
► Any scheduling updates or mid-study changes are synced with DFengage.Offline
► Setup and visit schedule information is stored in the app on device so that activities can be completed while offline.
► Notifications are sent while offline according to schedule from last sync.
► Once back online with app open and active, the data is synced with DFdiscover and removed from device. -
Hello Aung Thura Htoo, thank you for sharing your experience and outlining the areas you aim to improve.
I completely agree that focusing on information technology skills, such as data management, statistics, programming, and cloud computing, is both strategic and practical for handling complex health data effectively.
Moreover, your emphasis on developing interpersonal skills such as teamwork, project management, and effective communication demonstrates a strong commitment to collaborative work and efficient knowledge sharing. These skills are indeed crucial for success in the field of health information technology which heavily relies on interdisciplinary collaboration. 🙂
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Hi Phyo, Thanks for sharing your dashboard, I just had a chance to look at your dashboard, and I have to say, I’m really impressed! The layout is clean and intuitive, making it super easy to navigate. I especially like how you’ve used picture icon coding to highlight key metrics. It adds a layer of usability that enhances the overall experience. Great job! I can see this being incredibly useful for anyone who needs to keep track of COVID-19 surveillance. Keep up the fantastic work!
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