Handbook ======== This handbook is intended for instructors who wish to create and implement new virtual learning sessions in cAIte. Implementation of new patients, feedback pipelines, and full learning sessions is carried out in collaboration with the TIME institute at the University Hospital Tübingen: `https://www.medizin.uni-tuebingen.de/de/das-klinikum/einrichtungen/institute/time `_ This document provides an overview of the steps required to create and integrate a virtual patient. Please note that cAIte is continuously evolving, and some details in this handbook may become outdated. Always verify information with the latest resources and updates from the TIME team. To access cAIte, you will be provided with a username and password by the cAIte Team. 1. AI-Driven Virtual Patients in cAIte --------------------------------------- The foundation of cAIte's learning experience is its AI-driven virtual patients. They provide a realistic, interactive environment where learners can practice clinical reasoning, patient communication, and scenario-based tasks safely and independently. Each virtual patient is individually generated and customizable, with defined personality traits, character background, communication style, and medical information. This ensures high role stability, allowing learners to engage in consistent, meaningful interactions across multiple sessions. The virtual patients are designed to be dynamic and responsive: learners can ask questions, explore patient histories, and receive context-appropriate answers. While the AI may vary responses slightly between chats, the underlying character and scenario remain coherent, providing a realistic yet controlled learning setting. By combining rich, well-defined characters with scenario-specific challenges, cAIte virtual patients offer a flexible and immersive platform for practicing skills, preparing for real-world patient interactions, and supporting reflective learning. 1.1 Currently Available Virtual Patient Cases (Selection) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Internal / General Medicine ^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. list-table:: :widths: 15 85 * - |img_ferdinand_wunderlich| - | **Ferdinand Wunderlich** | 48-year-old administrative employee | Initial diagnosis of type 2 diabetes mellitus * - |img_oskar_haase| - | **Oskar Haase** | 81-year-old retiree | Pneumonia * - |img_sabrina_hummel| - | **Sabrina Hummel** | 20-year-old student | Hypothyroidism * - |img_vera_wagner| - | **Vera Wagner** | 42-year-old travel agency manager | Deep vein thrombosis * - |img_markus_baecker| - | **Markus Bäcker** | Pancreatic carcinoma | → minimizing coping style **or** → despairing coping style * - |img_markus_sonnenbichler| - | **Markus Sonnenbichler** | 62-year-old plant mechanic | Localized prostate carcinoma * - |img_daniel_weber| - | **Daniel Weber** | 27-year-old lawyer | Myxoid liposarcoma * - |img_julia_martins| - | **Julia Martins** | 52-year-old office clerk | Gender medicine: myocardial infarction * - - **48 cases involving 12 primary symptoms in general practice/emergency situations, each with 4 levels of urgency, to enhance decision-making skills** (fever, abdominal pain, back pain, headache, cough, sore throat, chest pain, visual disturbances, rash, dysuria, diarrhea, depressive symptoms) * - - **3 emergency cases from the internal medicine emergency department** (sepsis, upper GI-bleeding, angina pectoris) Obstetrics ^^^^^^^^^^ .. list-table:: :widths: 15 85 * - |img_marie_zeiser| - | **Marie Zeiser** | 18-year-old student | Unexpected pregnancy * - |img_ronja_klemm| - | **Ronja Klemm** | 33-year-old sales assistant | Initial consultation – pregnancy Mental / Psychosomatic Disorders ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. list-table:: :widths: 15 85 * - |img_samuel_richter| - | **Samuel Richter** | 56-year-old merchant | Alcohol dependence * - |img_eva_gerdes| - | **Eva Gerdes** | 66-year-old former executive assistant | Mild depressive episode * - |img_lauretta_turm| - | **Lauretta Turm** | 27-year-old retail salesperson | Recurrent depressive episode * - |img_tobias_wagner| - | **Tobias Wagner** | 28-year-old law student | Moderate depressive episode * - |img_thibault_bellier| - | **Thibault Bellier** | 60-year-old auto mechanic | Severe depression * - |img_andreas_petersen| - | **Andreas Petersen** | 53-year-old manager | Symptoms of depression and burnout * - |img_annalena_taube| - | **Annalena Taube** | 28-year-old student | Recurrent depression * - |img_gerhard_anton| - | **Gerhard Anton** | 53-year-old bus driver | Chronic pain disorder with somatic and psychological factors * - |img_agnes_baumgartner| - | **Agnes Baumgartner** | 46-year-old secretary | Agoraphobia and panic disorder * - |img_katharina_lodde| - | **Katharina Lodde** | 32-year-old graphic designer | Bipolar disorder, currently hypomanic * - |img_kristin_kunz| - | **Kristin Kunz** | 52-year-old teacher | Generalized anxiety disorder * - |img_liddy_noeter| - | **Liddy Nöter** | 22-year-old medical student | Borderline personality disorder 1.2 Creating a New Patient Case ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This section guides you through creating a new virtual patient with all its details. Below, you will find an overview of the required steps, along with estimated durations for each task. **Estimated Timeframe for Creating a New Patient in cAIte:** - Total time: ~1–3 weeks (6–16 working hours) - Initial patient case creation: 1–3 hours - Initial testing of the patient: 1–4 hours (the more thorough, the better) - Adjusting patient case information: 1 hour - Testing and optimizing adjusted patient case information: 1–4 hours (the more thorough, the better) - *(optional)* Customizing cAIte-specific scoring/feedback with the cAIte team: 1–2 weeks - Final adjustments: 0.5 hours - Basic documentation: 1 hour 1.2.1 Creating the Patient Case – Step by Step ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ **1. Developing the content of the case vignette** On the cAIte webpage, navigate to “To teachers view” and select “new case”. To create a new patient case, all relevant information must be entered through the web interface form. This includes: - Main Case Information (including Case Name, Description for Users, User Role, and Role of Virtual Character) - Config Options - Personal Data / Description of the virtual Patient - Character Background - Character Communication Style - Medical History - General Scoring / Feedback - Case-Specific Scoring / Feedback Some fields refer to the personality and character of the virtual patient, others to medical information, depending on the scenario. The required fields and effort vary depending on the selected clinical scenario (e.g., Anamnesis Consultation, Breaking Bad News, etc.), the patient's Medical History, and the type of Scoring / Feedback chosen. If you decide to create a new Feedback Pipeline, this usually needs to be done **before** creating a complete virtual patient (see below). Medical information is usually provided in the form of example answers that guide the patient's responses. **Guideline for Placeholders** The following placeholders can be used to manage the visibility and scoring of fields: - ``_duplicate_`` - Will appear in scoring but not in the prompt, as it's included in a non-scoring field. - ``_noscore_`` - Will appear in the prompt but not in scoring. - ``_ignore_`` - Will appear in neither the prompt nor scoring. .. note:: - The level of detail in character descriptions can vary by use case; more detailed descriptions generally lead to higher role stability during interactions. - If the virtual patient is asked a question it has no answer for, the LLM will generate a spontaneous response. These responses are generally consistent with the character, but can vary across different conversations. - Avoid repeating information across multiple form fields; redundant entries can unintentionally amplify certain personality traits. - To test the created patient case via chat, the data must first be compiled by the cAIte Team. You will then receive a link that allows you to conduct chat sessions. - After each adjustment of the patient case information, the link must be updated by the cAIte Team to reflect the changes. **Creating a visual representation of the virtual patient** - Generate an image using an AI image generator that reflects the patient's age, gender, and general appearance. - Ensure consistency between the image and the defined character profile. - Use the image to enhance realism and user engagement within the learning session. - Optionally, use an LLM to generate or refine the image prompt for better results. - The picture will be displayed during the chat. **2. Testing the Patient Case** Testing is a critical step in creating a reliable patient case. Since cAIte uses a complex system to ensure adherence to the case information, unexpected interactions can occur. Many of these issues can be detected and resolved during thorough testing. Once all components of the patient case have been defined, the cAIte Team makes it accessible for testing via a link. To test the patient case, please follow the steps below: a. **Conduct a thorough "good student" interview** Perform the task requested from students as accurately as possible. Aim to achieve all points in the feedback. Try to ask some questions beyond your example answers to check whether the character stays in its role. The session should last at least 20 minutes to ensure that cAIte maintains relevant information throughout the conversation. Document any shortcomings in feedback or dialogue. b. **Adjust technical configuration** If scoring or feedback does not align with expectations and cannot be corrected within the case description, contact the cAIte Team for support. After adjustments, repeat step a) or proceed to the next step. c. **Conduct a "bad student" interview** Simulate a student who misunderstands the task or lacks necessary information. Follow the task instructions but try to score poorly. Observe feedback and scoring to ensure cAIte accurately captures suboptimal student behavior. Document your findings. d. **Refine case information** Fill in missing details, clarify feedback points, and optimize scoring. Engage with the cAIte Team if necessary. e. **Final review of wording and instructions** Conduct a final check of the case vignette and the . Ensure instructions are clear and minimize confusion. Determine whether translations into English or German are needed. f. **Pilot the case** Freeze the case and run a pilot session with a colleague to validate usability and correctness. **3. Basic documentation** If the piloting phase is successful: - Take screenshots of all relevant parts of the patient case. - Export all case information from cAIte. - Write a short methods section / technical documentation describing how the case was created, including setup, feedback configurations, and any special considerations. 2. AI-generated, Automatic Feedback in cAIte --------------------------------------------- At the heart of cAIte lies its AI-generated, automatic feedback - the central feature that sets the platform apart. We believe that individualized, high-quality feedback is essential for fully realizing the benefits of AI-supported teaching and learning. A great deal of time and effort goes into designing, refining, and continuously improving this feedback system. By providing learners with real-time guidance and post-interaction analysis, cAIte helps users reflect on their performance, recognize strengths and areas for improvement, and practice clinical communication skills in a safe and controlled environment. After interacting with a virtual patient, cAIte delivers two types of automated feedback: **1. Post-Interaction (Category-based) Feedback** Post-interaction feedback is generated after the chat has been completed. It is based on the entire conversation history and provides a more comprehensive evaluation. Examples include assessing whether all relevant questions were asked and whether key information was appropriately elicited. **2. Live Feedback** Live (or real-time) feedback is provided during the conversation with the virtual patient. It focuses on specific communication behaviors as they occur. Examples include the use of communication techniques such as open questions, mirroring, or other conversational strategies. **Choice of feedback options** The feedback configuration is selected during the development of the patient case. If you wish to use an existing feedback setup, you can choose it from the available options within the input form and provide any required additional information. The cAIte Team will help you to navigate to the correct options. If you intend to create a new feedback pipeline, the corresponding feedback categories must be defined in advance, before the patient case form can be fully completed. Once filled with patient information, these categories cannot be modified. However, the definitions and descriptions of the feedback categories can still be refined and adjusted at a later stage. 2.1 Currently Available Feedback Pipelines ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Post-Interaction (Category-Based) Feedback ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ **General/Internal Medicine History Taking** [2]_ .. figure:: https://caite-main-static-public.s3.amazonaws.com/docs/overview_screenshots/anamnese_kategorien.png :width: 100% :align: center ---- **Condition-Specific History Taking** Hypomania, Alcohol use disorder, Borderline personality disorder, Agoraphobia, Panic disorder. ---- **Disorder-Specific History Taking (Depression):** Diagnosis and Severity Classification [3]_ .. figure:: https://caite-main-static-public.s3.amazonaws.com/docs/overview_screenshots/ss1.png :width: 100% :align: center ---- **Psychopathological Assessment** *(not yet published)* ---- **SBAR: Interprofessional Handover Protocol** *(not yet published)* Pipeline based on a human expectation framework for structured clinical handovers. .. figure:: https://caite-main-static-public.s3.amazonaws.com/docs/overview_screenshots/ss2.png :width: 100% :align: center ---- **Maternity Record Documentation (Medical History)** *(not yet published)* Evaluates the documentation of medical history in the maternity record. .. figure:: https://caite-main-static-public.s3.amazonaws.com/docs/overview_screenshots/ss3.png :width: 100% :align: center ---- **SPIKES: Delivering Bad News** *(not yet published)* .. figure:: https://caite-main-static-public.s3.amazonaws.com/docs/overview_screenshots/ss4.png :width: 100% :align: center ---- **In Development** - Shared Decision Making - conversation structure - OPTION - Shared Decision Making evaluation - And more Live-Feedback [4]_ ^^^^^^^^^^^^^^^^^^ **Communication Techniques** - NURSE (live) - WWSZ (live) - Empathic communication according to Rogers (text-based feedback) - Interventions (conversation-based) - Motivational Interviewing / DBT (live) .. figure:: https://caite-main-static-public.s3.amazonaws.com/docs/overview_screenshots/ss5.png :width: 100% :align: center .. figure:: https://caite-main-static-public.s3.amazonaws.com/docs/overview_screenshots/ss6.png :width: 100% :align: center ---- **Real-Time Documentation** - SORKC (behavioral analysis) ---- **In Pilot Phase** - Additional applications currently under development. 2.2 Creating a New Automatic Feedback Pipeline ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Developing a new automatic feedback pipeline is a more advanced task, and time requirements may vary depending on complexity and iteration cycles. **Estimated Timeframe:** - Total time: ~6 weeks (10-60 working hours) - Initial feedback design: 3-10 hours - Initial testing: 1-4 hours - Adjusting feedback rules: 0.5 hours - Testing adjusted feedback: 1-5 hours - Piloting with ~10 chats: 5-10 hours - Annotating pilot data: 5-10 hours - Feedback optimization: 5-10 hours - Final adjustments: 0.5 hours - Basic documentation: 1 hour 2.2.1 Creating a New Automatic Feedback Pipeline - Step-by-Step ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Developing a new automatic feedback pipeline is an iterative process that involves design, testing, piloting, and refinement. The steps below follow a typical workflow: **1. Initial Feedback Design** Define the feedback structure using the cAIte interface. - Navigate to "Medical history categories", enter a name for the category and choose "Add category". - Enter feedback categories and corresponding descriptions in the provided form. - Ensure that each category clearly describes the behavior or criterion to be assessed. Optionally, use an LLM to refine the prompt for the description of the feedback categories. **2. Initial Testing** Test the feedback pipeline using self-conducted chats. - Perform several example conversations with the virtual patient. - Check whether the feedback aligns with the intended categories. **3. Adjust Feedback Descriptions** Refine the feedback categories and descriptions. - Clarify and specify category definitions where needed. - Remove or merge unclear or redundant categories. - Finalize the initial set of feedback categories. **4. Re-Testing** Test the adjusted feedback pipeline again. - Conduct additional chats to verify improvements. - Ensure that feedback is applied consistently and meaningfully. **5. Pilot Phase (Data Collection)** Collect a set of pilot chats for systematic evaluation. - Aim for at least 5-10 chat sessions (more is preferable). - Include different users to increase variability and realism. **6. Annotation of Pilot Chats** Annotate the collected chats based on the defined feedback categories. - Each chat is coded on a binary level: - ``0`` = criterion not present - ``1`` = criterion present - Ideally, 1-2 annotators should perform the coding. **7. Feedback Optimization (with cAIte Team)** Based on the annotated data, cAIte identifies false positives (feedback triggered incorrectly) and false negatives (feedback missing when it should be present). - Refine the underlying prompting and logic in collaboration with the cAIte Team. - This step may need to be repeated iteratively. **8. Final Adjustments** - Incorporate final refinements after optimization. Ensure stability and consistency across different chats. **9. Documentation** - Create basic documentation of the feedback pipeline. Describe categories, definitions, and intended use. .. warning:: This process does not constitute a formal validation. For validation, the feedback pipeline must be tested in a controlled real-world or teaching setting, followed by additional annotations based on real user interactions. 3. Questionnaires in cAIte --------------------------- In cAIte, questionnaires provide a flexible way to enhance learning sessions and gather structured data. They can be integrated as part of the learning experience - for example, through self-assessment questionnaires that support reflection and skill development - or used for research purposes, such as evaluating the effectiveness of a learning scenario or collecting data for validation studies. While questionnaires operate independently of the virtual patient and automated feedback, they offer a powerful tool to complement the AI-driven learning environment and capture additional insights from participants. **Design Options:** - Questionnaires can be created from scratch or based on existing, validated instruments from publications. - The type of questions, answer formats, and page layout must be individually discussed and defined with the cAIte Team. 3.1 Creating a Questionnaire - Step by Step ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. **Prepare Default Questionnaire** - Review existing questionnaires for similar content to avoid duplication. 2. **Request Implementation** - Contact the cAIte Team with the full list of questions and answer options. Clearly describe which data should be collected and the rationale behind each question. 3. **Test Questionnaire** - Once implemented, test all questions and answer options. Verify that the data export works correctly and includes all required information. Perform testing at least 2 weeks before data collection starts. 4. **Final Adjustments** - Check correct display, page layout, and any translation requirements (English / German). **Estimated Timeframe:** - Total: ~3 weeks (3–7 working hours, depending on complexity) 4. Creating Content Pages -------------------------- - Learning sessions can include pages with text and/or images, for example to provide background content. - PowerPoint presentations cannot be directly inserted; only static content in supported formats is allowed. - Text and images must be formatted according to the following specifications: .. code-block:: json { "title": "Example Title", "paragraphs": [ {"content": ["Example Content"]}, {"title": "Example Title 2", "content": ["Example Content 2"]}, { "alt": "A diagram of the shared decision making flow", "path": "tempaccess/ws2526_sdm_sdmsteps.png", "type": "image" } ] } - Optionally, an LLM can assist in formatting text. - If you provide the formatted text, the cAIte Team can integrate the content pages. 5. Creating the Web Interface Page Flow ----------------------------------------- The page flow is where all components of a cAIte learning session - virtual patient, questionnaires, content pages, demographic forms, task instructions, and feedback - are brought together in a single, accessible web interface. It is created by the cAIte Team based on the instructor’s specifications. This step completes the creation of the learning session and determines how learners experience the scenario. Careful design and thoughtful arrangement of pages not only ensure smooth navigation but also make the session more engaging and visually appealing. Investing time in this stage pays off: a well-structured page flow enhances learner focus, supports the intended learning objectives, and ensures that both teaching and data collection run seamlessly. **Instructor Responsibilities:** - Decide which pages are included in the flow. Examples: - Content / information pages - Pictures - Questionnaires - Demographic data collection - Data privacy confirmation - Task instructions - Virtual patient chat - Feedback pages - Define the order of pages and how users progress through the flow. - Define how long the link needs to be accessible. - Test the page flow thoroughly: a. **Verify page flow and forms** - Start the case and click through all displayed pages. Only conduct a brief interaction with the virtual patient. Ensure that the **correct forms** are shown in the **correct order**. b. **Fix form issues** - If forms are missing, incorrectly displayed, or duplicated, resolve these issues. The page flow can only be edited by the cAIte Team. c. **Check research data export** *(if applicable)* - For research projects, verify (together with the cAIte Team) that all relevant data from the interview can be exported. Ensure all necessary data for your research question is available. **cAIte Team Responsibilities:** - Implement the page flow in the web interface. - Provide a link for testing and for participants to access the case. **User Access:** Using the provided link, learners can complete the flow either within a class session or remotely on their own desktop. **Estimated Timeframe:** - Total: ~1-2 weeks (1-5 working hours, depending on complexity) 6. Data Sharing ---------------- As cAIte is both a learning and research tool, we aim to generate and publish insights on its usage and effectiveness as an AI-based educational platform. For this purpose, data sharing agreements are typically established at the beginning of a collaboration. These agreements define the conditions for data use, as well as potential authorship and contributions related to different research questions, before any data is collected. Our experience shows that establishing a clear data sharing agreement in advance helps to prevent misunderstandings and resolve potential disputes at a later stage. 7. Costs --------- Depending on the amount and complexity of feedback and scoring, the costs can vary considerably. As a rough estimate, the cost per 20-minute chat ranges from approximately **???** to **???** euros. References ---------- .. [2] Holderried F, Stegemann-Philipps C, Herrmann-Werner A, Festl-Wietek T, Holderried M, Eickhoff C, u. a. A Language Model-Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study. JMIR Med Educ. 16. August 2024;10:e59213. doi:10.2196/59213 PubMed PMID: 39150749; PubMed Central PMCID: PMC11364946. .. [3] Holderried F, Sonanini A, Philipps A, Stegemann-Philipps C, Herschbach L, Festl-Wietek T, u. a. Using AI to Train Future Clinicians in Depression Assessment: Feasibility Study. JMIR Med Educ. 12. Februar 2026;12:e87102. doi:10.2196/87102 .. [4] Herschbach L, Festl-Wietek T, Stegemann-Philipps C, Sonanini A, Herrmann B, Erschens R, u. a. Evaluation of an AI-Based Chatbot Providing Real-Time Feedback in Communication Training for Mental Health Care Professionals: Proof-of-Concept Observational Study. J Med Internet Res. 28. November 2025;27:e82818. doi:10.2196/82818 .. Image substitutions for patient avatars .. |img_ferdinand_wunderlich| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/ferdinand_wunderlich.png :width: 80px .. |img_oskar_haase| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/oskar_haase.png :width: 80px .. |img_sabrina_hummel| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/sabrina_hummel.png :width: 80px .. |img_vera_wagner| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/vera_wagner.png :width: 80px .. |img_markus_baecker| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/markus_baecker.png :width: 80px .. |img_markus_sonnenbichler| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/markus_sonnenbichler.png :width: 80px .. |img_daniel_weber| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/daniel_weber.png :width: 80px .. |img_julia_martins| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/julia_martins.png :width: 80px .. |img_marie_zeiser| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/marie_zeiser.png :width: 80px .. |img_ronja_klemm| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/ronja_klemm.png :width: 80px .. |img_samuel_richter| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/samuel_richter.png :width: 80px .. |img_eva_gerdes| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/eva_gerdes.png :width: 80px .. |img_lauretta_turm| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/lauretta_turm.png :width: 80px .. |img_tobias_wagner| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/tobias_wagner.png :width: 80px .. |img_thibault_bellier| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/thibault_bellier.png :width: 80px .. |img_andreas_petersen| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/andreas_petersen.png :width: 80px .. |img_annalena_taube| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/annalena_taube.png :width: 80px .. |img_gerhard_anton| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/gerhard_anton.png :width: 80px .. |img_agnes_baumgartner| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/agnes_baumgartner.png :width: 80px .. |img_katharina_lodde| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/katharina_lodde.png :width: 80px .. |img_kristin_kunz| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/kristin_kunz.png :width: 80px .. |img_liddy_noeter| image:: https://caite-main-static-public.s3.amazonaws.com/docs/patients/liddy_noeter.png :width: 80px