# Designing feedback loop to improve system design. related: - [[Principles in designing Digital interventions]] - [[Book - Thinking in Systems - A primer]] - [[Book - Weapons of Math Destruction - Cathy O'Neil]] **Human:** What's the definition of feedback loop in this context? *** **Assistant:** In the context of designing systems and algorithms, a feedback loop refers to a process of collecting data on the performance of the system or algorithm and using that data to improve its performance. Feedback loops can be designed to operate at different levels and time scales, depending on the specific application and goals of the system. At a high level, a feedback loop typically involves the following steps: 1. Collect data: Collect data on the performance of the system or algorithm, such as user behavior, system response times, or other metrics that are relevant to the system's goals. 2. Analyze data: Analyze the data to identify patterns or trends in the system's performance and identify areas where improvements can be made. 3. Make adjustments: Use the insights gained from the data analysis to make adjustments to the system or algorithm, such as modifying the input parameters or changing the decision-making criteria. 4. Test and evaluate: Test the adjusted system or algorithm using new data and evaluate its performance to determine whether the adjustments have improved its performance. 5. Repeat: Continuously collect data, analyze it, make adjustments, and evaluate performance, in an ongoing process of improvement and refinement. In this context, a feedback loop is a key component of a continuous improvement process, where the system or algorithm is constantly adapting and improving based on new data and insights. The goal is to optimize the system's performance over time and ensure that it is meeting the needs of its users or stakeholders. *** **Assistant:** Here are a few additional points to consider when designing feedback loops for systems and algorithms: 1. Feedback loops can be designed to operate at different levels and time scales, depending on the specific application and goals of the system. For example, a feedback loop for a real-time system may need to operate at a much faster time scale than a feedback loop for a longer-term decision-making process. 2. The data used to inform the feedback loop should be relevant, accurate, and timely. It's important to carefully choose the data sources and metrics used in the feedback loop to ensure that they are aligned with the system's goals and provide meaningful insights into its performance. 3. Feedback loops can be designed to incorporate different types of feedback, including user feedback, automated feedback from the system itself, and feedback from external sources. It's important to consider the trade-offs and benefits of each type of feedback and use a combination of feedback sources where appropriate. 4. It's important to consider the ethical implications of the feedback loop and ensure that it is transparent, explainable, and respectful of privacy and autonomy. This includes considering the potential biases in the data and algorithms used in the feedback loop and taking steps to mitigate them. By considering these points, you can design feedback loops that are effective, efficient, and responsible, and that help to continuously improve the performance of your system or algorithm. ---- **Human:** Be a data scientists, AI ethicist for me. I am designing a system that predict patient's mental state changes and early alert system to case managers. I know that we need to build in feedback loop to improve the design, the algorithms, how do i go about doing it? *** **Assistant:** As a data scientist and AI ethicist, I would recommend the following steps to incorporate a feedback loop into your system for predicting patient's mental state changes and providing early alerts to case managers: 1. Define the metrics: Start by defining the metrics that you will use to measure the performance of your system. These metrics should be aligned with the goals of your system, which in this case is predicting mental state changes and alerting case managers. Examples of metrics include accuracy, precision, recall, F1 score, and AUC. 2. Collect feedback data: Collect feedback data from the case managers who receive the alerts. This feedback data can come in the form of ratings or feedback on the accuracy of the alerts and how helpful they were in managing patient care. 3. Analyze the feedback data: Analyze the feedback data to identify patterns and areas for improvement in the system. Use this data to inform any necessary updates to the algorithms and design of the system. 4. Test the updated system: Test the updated system using real-world data and evaluate its performance using the defined metrics. 5. Iterate and improve: Based on the results of the testing, iterate and improve the system. Continuously collect feedback data and analyze it to make ongoing improvements. 6. Consider ethical implications: As an AI ethicist, it is important to consider the ethical implications of the system. Make sure that the system is transparent, explainable, and respects the privacy and autonomy of the patients. Also, consider potential biases in the data and algorithms, and take steps to mitigate them. By incorporating a feedback loop into your system and continuously iterating and improving, you can ensure that the system is effective in predicting mental state changes and providing early alerts to case managers. Additionally, by considering the ethical implications of the system, you can ensure that it is both effective and responsible. *** **Human:** How do i do step 2? What kind of questions to ask? what metrics can be considered? *** **Assistant:** To collect feedback data, you can ask case managers to provide their ratings or feedback on the alerts they received and how helpful they were in managing patient care. Here are some examples of questions you can ask: 1. How accurate were the alerts in predicting changes in the patient's mental state? 2. How helpful were the alerts in managing patient care? 3. Were there any false alarms or missed alerts? If so, how did that impact patient care? 4. Were the alerts provided in a timely manner? 5. Was the information provided in the alerts clear and understandable? In terms of metrics, some examples you can consider include: 1. True positive rate: The proportion of alerts that correctly predicted a mental state change. 2. False positive rate: The proportion of alerts that incorrectly predicted a mental state change. 3. Response time: The time between when an alert is generated and when it is received by the case manager. 4. Accuracy: The proportion of all alerts that were correctly predicted, both positive and negative. 5. Precision: The proportion of positive alerts that were correctly predicted. By collecting feedback data and analyzing these metrics, you can gain valuable insights into the performance of your system and identify areas for improvement. *** **Human:** But how do i validate whether indeed a mental state change has occured? Who can give me the truth? As in, can case managers, the patient itself give me that answer? Will there be biases or subjectivity involved? *** **Assistant:** Validating whether a mental state change has occurred can be challenging because mental health is subjective and can be influenced by various factors, including individual differences, environmental factors, and life events. In addition, different people may have different interpretations of what constitutes a mental state change. To address this challenge, you can consider using a combination of objective and subjective measures to validate whether a mental state change has occurred. Objective measures include data such as changes in vital signs, medication adherence, and the frequency and duration of certain behaviors or activities. Subjective measures include self-reported data from the patient or their family members, as well as assessments and observations from mental health professionals. In terms of who can provide the truth, mental health professionals such as psychiatrists, psychologists, or licensed clinical social workers are trained to assess and diagnose mental health conditions. However, it is important to recognize that even mental health professionals can have biases and subjectivity in their assessments, and it is important to consider multiple perspectives when evaluating mental health. As for biases and subjectivity, it is essential to be aware of these potential issues and take steps to mitigate them. For example, you can consider using standardized assessment tools and protocols, using multiple sources of data, and involving a diverse group of mental health professionals in the validation process. Additionally, it is important to regularly evaluate and refine the algorithms and models used in the system to ensure that they are not perpetuating biases. *** **Human:** What do you think of this design. 1. I use standardised assessment tools, and change them into EMA to assess the patient periodically. 2. I use clinical interviews by professionals to do mental state assessments. 3. I correlate that with the patient objective digitial phenotype data. 4. I collect feedback from case manager on How accurate were the alerts in predicting changes in the patient's mental state? How helpful were the alerts in managing patient care? Were there any false alarms or missed alerts? If so, how did that impact patient care? Were the alerts provided in a timely manner? Was the information provided in the alerts clear and understandable? *** **Assistant:** The design you have proposed is a good starting point for developing a system that predicts mental state changes and provides early alerts to case managers. Here are some additional thoughts: 1. Using standardized assessment tools and converting them into EMA is a good approach to periodically assess the patient's mental state. However, it's important to ensure that the EMA tool accurately captures the nuances of the standardized assessment and is validated before using it to assess patients. 2. Clinical interviews conducted by mental health professionals are an important component of mental state assessments. However, it's important to consider the availability and feasibility of conducting clinical interviews regularly. 3. Correlating the patient's objective digital phenotype data with the clinical assessments can provide a more holistic view of the patient's mental state. However, it's important to ensure that the digital data is accurate and unbiased and that the correlation analysis is statistically sound. 4. Collecting feedback from case managers is crucial for improving the system's performance and ensuring that it's meeting their needs. It's important to ensure that the feedback is collected in a standardized and consistent way and that it's used to iteratively improve the system. Overall, the design you have proposed is a good starting point, but it's important to continue iterating and improving the system based on the feedback received and ongoing evaluation. Additionally, it's important to consider the ethical implications of the system and take steps to mitigate potential biases and ensure that patient privacy and autonomy are respected. *** **Human:** How to track whether this early detection system changes how the case manager work compare to their usual business? *** **Assistant:** To track whether the early detection system changes how case managers work compared to their usual business, you can consider the following: 1. Establish a baseline: Before implementing the early detection system, establish a baseline for how case managers typically work, including their workflows, communication patterns, and decision-making processes. This can include conducting surveys or interviews with case managers, or analyzing existing data on their performance. 2. Implement the early detection system: Implement the early detection system and provide training to case managers on how to use it effectively. 3. Monitor usage and feedback: Monitor the usage of the system and collect feedback from case managers on how it is impacting their workflows, communication, and decision-making. This can include surveys, interviews, or analyzing system logs. 4. Compare to the baseline: Compare the usage and feedback data to the established baseline to identify any changes in case managers' workflows, communication, or decision-making processes. 5. Identify areas for improvement: Use the data collected to identify areas where the early detection system is having a positive impact on case manager workflows, as well as areas where it may need improvement. This can include providing additional training or support for case managers or making adjustments to the system's design or functionality. By tracking how the early detection system is impacting case manager workflows and comparing it to the established baseline, you can identify any changes and make ongoing improvements to the system. Additionally, by involving case managers in the feedback and improvement process, you can ensure that the system is meeting their needs and improving patient care. *** **Human:** What kind of information should be captured as the baseline? can you list them? *** **Assistant:** When establishing a baseline for how case managers typically work before implementing the early detection system, you can consider capturing the following information: 1. Workflow: Document the case manager's current workflow and process for managing patient care. This can include how they receive information about patients, how they communicate with other healthcare professionals, and how they track patient progress and outcomes. 2. Communication: Document the case manager's communication patterns with other healthcare professionals, including the frequency and methods of communication. 3. Decision-making processes: Document the case manager's decision-making processes, including how they assess patient needs, develop care plans, and make referrals to other healthcare professionals. 4. Performance metrics: Collect data on the case manager's performance metrics, such as the number of patients managed, the time spent on patient care, and the number of referrals made. 5. Patient outcomes: Collect data on patient outcomes, such as hospitalization rates, emergency department visits, and patient satisfaction. 6. Training and support: Document the training and support currently provided to case managers, including any ongoing education or professional development opportunities. By capturing this information, you can establish a baseline for how case managers typically work and identify areas where the early detection system may have the greatest impact. Additionally, you can use this information to set benchmarks and track progress over time as the early detection system is implemented and refined. *** **Human:** Is there any book that's related to designing feedback loops into system? I am a beginner, please recommend books that are easier to understand. *** **Assistant:** Yes, there are several books related to designing feedback loops into systems. Here are a few recommendations that are easier to understand: 1. "The Lean Startup" by Eric Ries: While not specifically focused on feedback loops, this book provides valuable insights into building products that are responsive to customer needs and quickly adapting based on feedback. It includes practical advice on designing experiments and measuring progress, which are important components of building effective feedback loops. 2. "Building a Feedback-Driven Organization" by David Sturt and Todd Nordstrom: This book provides a step-by-step guide to building feedback loops into organizational processes, including how to collect feedback, analyze it, and use it to drive continuous improvement. 3. "Feedback That Works: How to Build and Deliver Your Message" by Sloan Weitzel: This book provides practical advice on how to give and receive feedback effectively, including how to frame feedback in a positive way, use specific examples, and deliver it in a way that is actionable and constructive. 4. "The Power of Feedback" by Joseph Folkman and John H. Zenger: This book provides insights into the power of feedback to drive performance improvement, including how to build a culture of feedback, use feedback to motivate employees, and design feedback systems that are effective. These books provide a good starting point for learning about feedback loops and how to design them effectively. They are written in an accessible style and include practical advice and examples that can be applied in a variety of contexts. *** **Human:** I want something more specific to feedback loop for systems, such as algorithms. Are there similar or differences? *** **Assistant:** Yes, there are books that are specifically focused on designing feedback loops for systems and algorithms. Here are some recommendations: 1. "Feedback Control of Computing Systems" by Joseph L. Hellerstein and Yixin Diao: This book provides a comprehensive introduction to feedback control theory and its applications to computing systems, including how to design feedback loops for optimizing system performance and ensuring stability. 2. "Building Intelligent Systems: A Guide to [[Machine Learning]] Engineering" by Geoff Hulten, David Hardtke, and Aileen Nielsen: This book provides practical guidance on designing feedback loops for [[machine learning]] systems, including how to incorporate feedback from users, evaluate the performance of the system, and adjust the system based on the feedback received. 3. "Control System Design Guide: Using Your Computer to Understand and Diagnose Feedback Controllers" by George Ellis: This book provides a practical guide to designing feedback loops for control systems, including how to use computer simulations to test and optimize feedback controllers. 4. "Adaptive Control: Algorithms, Analysis and Applications" by Ioan Doré Landau, Rogelio Lozano, and Mohammed M'Saad: This book provides a comprehensive introduction to adaptive control theory, including how to design feedback loops that can adapt to changes in the system or the environment. These books focus specifically on designing feedback loops for systems and algorithms, and they provide practical guidance on how to apply feedback control theory to various contexts. They are more technical than the previous recommendations but provide a deeper understanding of how feedback loops can be used to optimize system performance and ensure stability. *** **Human:** What will be a false alarm mean in mental state detection based on Digital phenotyping? *** **Assistant:** In the context of mental state detection based on digital phenotyping, a false alarm would occur when the system alerts a case manager or other healthcare professional that a patient's mental state has changed when it has not actually changed. False alarms can be caused by a variety of factors, including errors in the data or algorithms used in the system, or changes in the patient's behavior or environment that are unrelated to their mental health. False alarms can lead to unnecessary interventions or actions, which can increase healthcare costs, cause anxiety or distress for the patient, and reduce the credibility and effectiveness of the system. To reduce the incidence of false alarms in mental state detection based on digital phenotyping, it's important to design the system with appropriate levels of sensitivity and specificity. This can be achieved through careful selection of the data sources and algorithms used in the system, as well as by incorporating feedback from case managers and other healthcare professionals to refine the system's performance over time. Additionally, it's important to provide case managers with clear and concise information about the alerts generated by the system, including the level of confidence in the alert and any relevant contextual information. This can help to reduce the likelihood of false alarms and ensure that case managers can respond appropriately to genuine changes in a patient's mental state. *** **Assistant:** Another way to reduce false alarms is to establish clear criteria for what constitutes a significant change in a patient's mental state. This can be based on standardized assessment tools, clinical guidelines, or other evidence-based criteria that have been validated for use in mental health settings. By establishing clear criteria for what constitutes a significant change, the system can be calibrated to be more sensitive to genuine changes in a patient's mental state, while reducing the likelihood of false alarms due to minor fluctuations in the patient's behavior. Finally, it's important to recognize that false alarms are an inherent risk in any system that relies on digital phenotyping to detect changes in a patient's mental state. Therefore, it's important to design the system with appropriate safeguards and mechanisms for follow-up and verification. This can include providing case managers with the ability to manually confirm or reject alerts generated by the system, as well as providing patients with the ability to report any inaccuracies or concerns with the system. Overall, false alarms can pose a significant challenge in mental state detection based on digital phenotyping, but by designing the system with appropriate levels of sensitivity and specificity, establishing clear criteria for what constitutes a significant change, and providing appropriate safeguards and mechanisms for follow-up and verification, it is possible to minimize the risk of false alarms and improve the effectiveness of the system. *** **Assistant:** Another way to address the risk of false alarms in mental state detection based on digital phenotyping is to consider the context in which the system is being used. This can include factors such as the patient's age, gender, cultural background, and other demographic or psychosocial factors that may impact their mental health. By taking these contextual factors into account, the system can be designed to be more sensitive and specific to the needs of individual patients, while reducing the risk of false alarms due to cultural or other biases. In addition to these technical considerations, it's also important to ensure that the system is designed in a way that respects patient privacy and autonomy, and that is consistent with ethical and legal standards for the use of personal health information. This can include providing patients with clear and transparent information about how their data will be used, obtaining their consent for participation in the system, and ensuring that their data is stored and processed securely and in compliance with relevant regulations and guidelines. Overall, reducing the risk of false alarms in mental state detection based on digital phenotyping requires a multifaceted approach that takes into account technical, contextual, ethical, and legal considerations. By designing the system with appropriate sensitivity and specificity, clear criteria for what constitutes a significant change, appropriate safeguards and mechanisms for follow-up and verification, and a respectful and ethical approach to patient privacy and autonomy, it is possible to minimize the risk of false alarms and improve the effectiveness of the system. *** **Assistant:** One additional consideration for addressing false alarms in mental state detection based on digital phenotyping is to use a multi-modal approach to data collection and analysis. This can involve integrating data from multiple sources, such as wearables, mobile apps, social media, and electronic health records, to provide a more comprehensive picture of the patient's mental health status. By using a multi-modal approach, the system can leverage the strengths of different data sources and algorithms to reduce the risk of false alarms and improve the accuracy of mental state detection. For example, wearables may provide valuable data on physical activity, [[sleep]] patterns, and physiological indicators of stress, while social media may provide insights into the patient's social support network and emotional state. However, it's important to be aware that integrating data from multiple sources can also increase the risk of false alarms if the data is not carefully curated and validated. Therefore, it's important to establish clear protocols for data collection, curation, and analysis, and to continually evaluate the performance of the system to ensure that it is effectively leveraging the multi-modal data. Overall, using a multi-modal approach to mental state detection based on digital phenotyping can be an effective way to reduce the risk of false alarms and improve the accuracy of the system. However, it requires careful consideration of the strengths and limitations of different data sources and algorithms, as well as ongoing evaluation and refinement of the system's performance over time. *** **Assistant:** Those are some key considerations for addressing the risk of false alarms in mental state detection based on digital phenotyping. However, it's worth noting that designing effective feedback loops and reducing the risk of false alarms is an ongoing process that requires continuous refinement and adaptation based on new data and insights. Therefore, it's important to establish a culture of continuous improvement, where case managers, healthcare professionals, and patients are encouraged to provide feedback on the system's performance, and the system is regularly evaluated and refined based on that feedback. This can involve using a variety of feedback mechanisms, such as surveys, interviews, focus groups, and usability testing, to gather input from stakeholders and identify areas for improvement. By adopting a culture of continuous improvement, it's possible to design mental state detection systems based on digital phenotyping that are responsive to the needs of patients and healthcare professionals, while minimizing the risk of false alarms and optimizing system performance over time. --- **Human:** How to look out for false negative? *** **Assistant:** In the context of mental state detection based on digital phenotyping, a false negative would occur when the system fails to detect a change in a patient's mental state when in fact there has been a change. False negatives can occur due to various factors, such as limitations in the data or algorithms used in the system, or changes in the patient's behavior or environment that are not adequately captured by the system. To look out for false negatives, it's important to design the system with appropriate levels of sensitivity and specificity, as well as to establish clear criteria for what constitutes a significant change in a patient's mental state. This can be based on standardized assessment tools, clinical guidelines, or other evidence-based criteria that have been validated for use in mental health settings. In addition to sensitivity and specificity, it's also important to consider the timeliness of the system's alerts. False negatives can occur if the system fails to detect changes in a timely manner, such as due to delays in data collection or processing. Therefore, it's important to design the system to operate in real-time or near real-time, and to establish appropriate thresholds for triggering alerts based on the level of urgency and importance of the change in mental state. Another way to look out for false negatives is to incorporate feedback from case managers and other healthcare professionals into the system's design and operation. This can involve soliciting feedback on the system's performance, identifying any missed alerts or false negatives, and using that feedback to refine the system's algorithms and improve its sensitivity and specificity over time. Overall, looking out for false negatives in mental state detection based on digital phenotyping requires a multifaceted approach that takes into account technical, clinical, and operational considerations. By designing the system with appropriate sensitivity and specificity, clear criteria for what constitutes a significant change, timely alerts, and incorporating feedback from case managers and other healthcare professionals, it is possible to minimize the risk of false negatives and improve the effectiveness of the system. One additional consideration for looking out for false negatives is to incorporate multiple data sources into the system's design. By leveraging multiple data sources, such as wearables, mobile apps, social media, and electronic health records, the system can capture a more comprehensive picture of the patient's mental health status, which can help to reduce the risk of false negatives and improve the accuracy of mental state detection. For example, wearables may provide data on physical activity and [[sleep]] patterns, while mobile apps may provide data on mood and emotional state, and social media may provide data on social support and social interactions. By integrating these data sources, the system can identify changes in a patient's mental state that may not be captured by any one data source alone, which can help to reduce the risk of false negatives and improve the accuracy of mental state detection. However, as with any multi-modal approach, it's important to carefully curate and validate the data from each source to ensure that it is accurate, reliable, and relevant to the patient's mental health status. Additionally, it's important to consider the potential biases in each data source and take steps to mitigate them, such as by incorporating demographic and contextual factors into the analysis. That's all for now. --- Exported on 13 April 2023 9:38 pm.