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| Name | Class |
|---|---|
| Universität zu Köln | UNKNOWN |
| Technische Hochschule Brandenburg | UNKNOWN |
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This study investigates the feasibility and impact of patient access to clinical notes written by their healthcare providers-a concept known as Open Notes. While international research has already demonstrated positive effects of Open Notes on patient empowerment and treatment outcomes, there is a lack of corresponding evidence for the German healthcare context. The quasi-experimental study therefore combines quantitative and qualitative methods to evaluate the effects of Open Notes on patient-reported outcomes as well as clinical practice.
The study is structured into five modules, each addressing specific research questions:
Module A - Summative Outcome Evaluation in Patients: Does the use of Open Notes lead to a significant increase in patient-reported outcomes such as empowerment and self-efficacy? Module B - Formative Process Evaluation with Stakeholders: What experiences do patients, relatives and clinicians have with Open Notes and what challenges and barriers arise in their use? Module C - Changes in Clinical Documentation: How do language style and content of clinical notes change when they are shared with patients as Open Notes? Module D - Optimization through Artificial Intelligence: Can clinical notes be processed using artificial intelligence in a way that makes them easier for patients to understand compared to conventional medical documentation? Module E - Integration into National Data Infrastructure: What technical, organizational and legal requirements must be met to successfully integrate Open Notes into the national telematics infrastructure and the electronic health record or routine care? The overall goal is to identify both patient-related outcomes and structural conditions for the sustainable implementation of Open Notes in the German healthcare system. The use of artificial intelligence is intended to further enhance patient-centeredness while reducing the burden on clinical staff.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Shared Notes Access | Experimental |
| |
| No Notes Access | No Intervention | Due to potential selection bias of the intervention group, which occurs due to voluntary participation in the intervention, a control group is surveyed at t0, which does not participate in the intervention, so that an adjustment for the systematic differences, if any, between the intervention and control group is possible at t0. The survey of a post-intervention control group (t1) serves to adjust for any time-varying effects so that a general time effect can be ruled out. n=91 patients with a pre-intervention and a post-intervention control group of n=91 patients |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Patient Portal-Based Access to Clinical Notes | Device | An intra-individual comparison of patient-reported outcomes is planned for the intervention group. For this purpose, the pre-intervention survey of the intervention group is conducted upon inclusion in the study, the post-intervention survey at the earliest after the intervention has been used once (accessing their open note). Due to potential selection bias of the intervention group, which occurs due to voluntary participation in the intervention, a control group is surveyed at t0, which does not participate in the intervention, so that an adjustment for the systematic differences, if any, between the intervention and control group is possible at t0. The survey of a post-intervention control group (t1) serves to adjust for any time-varying effects so that a general time effect can be ruled out. |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Patient Empowerment Measured by the Health Care Empowerment Inventory | This primary outcome measures change in patient empowerment using the Health Care Empowerment Inventory. The HCEI is a validated self-report instrument assessing a patient's sense of being informed, engaged, committed, collaborative, and tolerant of uncertainty in relation to their health care. Scores range from 1 to 5 per item and are aggregated for a total score. Data will be collected via the PEPPPSY patient portal before and after the first access to Open Notes. Analyses include intra-individual pre/post comparisons in the intervention group and comparisons with a non-exposed control group. Outcome modeling will take frequency of Open Notes use into account. | From baseline to follow-up, up to 24 months per participant in the intervention group |
| Stakeholder Experiences with Open Notes Assessed by Semi-Structured Interviews and Focus Groups Using a CFIR-Based Guide | This outcome captures user experiences with Open Notes, including knowledge and beliefs about the intervention, attitudes toward digital access to clinical notes, and digital literacy. Data will be collected through 88 semi-structured interviews (44 patients, 20 relatives, 24 practitioners) and 16 focus groups (~80 participants), guided by an interview protocol based on the Consolidated Framework for Implementation Research (CFIR). This topic is primarily addressed in the interviews. Data will be analyzed using qualitative content analysis. | Four iterative sampling points over a 20-month period (t0 to t3, in 3-month intervals) |
| Perceived Advantages and Challenges of Open Notes Assessed by Semi-Structured Interviews and Focus Groups Using a CFIR-Based Guide | This outcome explores stakeholder views on the benefits and challenges of using Open Notes. Topics include perceived usefulness, empowerment, relative advantages, usability, and individual characteristics such as health literacy. Data will be collected through 88 semi-structured interviews and 16 focus groups using a CFIR-based guide. This outcome is primarily addressed in the interviews. Data will be analyzed using qualitative content analysis. | Four iterative sampling points over a 20-month period (t0 to t3, in 3-month intervals) |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Shared Decision Making Measured by the 9-Item Shared Decision Making Questionnaire (SDM-Q-9) | Assesses perceived shared decision making using the SDM-Q-9. This 9-item self-report instrument uses a 6-point Likert scale and is transformed to a 0-100 total score, with higher scores indicating more shared decision making. Data will be collected via the PEPPPSY patient portal before and after first access to Open Notes. Analyses include intra-individual pre/post comparisons and comparisons with a non-exposed control group. Outcome modeling will take frequency of use into account. |
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Inclusion Criteria:
Patients (General, Module A & D):
For Module D:
Patients (Control Group - Module A):
•No requirement for access to an internet-enabled device
Healthcare Practitioners (Modules A & D):
Relatives (Module B):
Experts (Module E):
Patients and Physicians (Module E):
Exclusion Criteria:
General:
Module D:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Julian Schwarz, MD, Specialist in Psychiatry | Contact | + 49 33638 83 501 | julian.schwarz@mhb-fontane.de | |
| Eva Meier-Diedrich, M.Sc. Psychology | Contact | eva.meier-diedrich@mhb-fontane.de |
| Name | Affiliation | Role |
|---|---|---|
| Florian Wurster, M.Sc. Health Services Research | University of Cologne | Study Director |
| Eva Meier-Diedrich, M.Sc. Psychology | Medical School Brandenburg | Study Director |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 19879711 | Background | Kriston L, Scholl I, Holzel L, Simon D, Loh A, Harter M. The 9-item Shared Decision Making Questionnaire (SDM-Q-9). Development and psychometric properties in a primary care sample. Patient Educ Couns. 2010 Jul;80(1):94-9. doi: 10.1016/j.pec.2009.09.034. Epub 2009 Oct 30. | |
| 23029184 | Background | Johnson MO, Rose CD, Dilworth SE, Neilands TB. Advances in the conceptualization and measurement of Health Care Empowerment: development and validation of the Health Care Empowerment inventory. PLoS One. 2012;7(9):e45692. doi: 10.1371/journal.pone.0045692. Epub 2012 Sep 19. |
| Label | URL |
|---|---|
| General information about the concept of open notes itself and the OpenNOTES-study | View source |
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| ID | Term |
|---|---|
| D001523 | Mental Disorders |
| D002908 | Chronic Disease |
| ID | Term |
|---|---|
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| Impact of Open Notes on Therapeutic Relationship and Communication Assessed by Semi-Structured Interviews and Focus Groups Using a CFIR-Based Guide | This outcome investigates how stakeholders perceive changes in therapeutic relationships and communication following the introduction of Open Notes. Topics include mutual trust, shared decision-making, and the involvement of relatives. Data are collected through semi-structured interviews and focus groups using a CFIR-based guide. This topic is primarily addressed in the interviews. Data will be analyzed using qualitative content analysis. | Four iterative sampling points over a 20-month period (t0 to t3, in 3-month intervals) |
| Perspectives on Future Implementation of Open Notes Assessed by Semi-Structured Interviews and Focus Groups Using a CFIR-Based Guide | This outcome captures stakeholder perspectives on the future integration of Open Notes into routine care. Topics include attitudes toward adoption, digital competencies, organizational readiness, infrastructure requirements, and implementation support needs (e.g., training, design adjustments). Data will be collected via semi-structured interviews and 16 focus groups using a CFIR-based guide. This outcome is primarily addressed in the focus groups. Data will be analyzed using qualitative content analysis. | Four iterative sampling points over a 20-month period (t0 to t3, in 3-month intervals) |
| Change in Linguistic Complexity of Clinical Notes Measured by Flesch-Kincaid Readability Index | This outcome assesses changes in linguistic complexity of clinical notes before and after Open Notes implementation using the Flesch-Kincaid readability index. A total of 20 notes before and 20 after implementation will be analyzed per study center using an online tool (https://conrics.com/features/). Higher scores indicate easier readability. Results will be compared across timepoints and study sites. | 6 months for pre-Open Notes documentation and 6 months for post-Open Notes documentation |
| Change in Length of Clinical Notes Measured by Word and Sentence Count | This outcome examines changes in the average length of clinical notes before and after Open Notes implementation. Word and sentence counts will be determined for 20 notes before and 20 after implementation per study center. Descriptive and inferential comparisons will be made between groups. | 6 months for pre-Open Notes documentation and 6 months for post-Open Notes documentation |
| Change in Thematic Content of Clinical Notes Measured by Linguistic Inquiry and Word Count (LIWC) | This outcome investigates whether the thematic focus of clinical notes changes following the implementation of Open Notes. LIWC software will be used to analyze 20 notes before and 20 after implementation per study center. Changes in thematic categories such as affective tone, cognitive processing, and personal pronouns will be examined and compared descriptively. | 6 months for pre-Open Notes documentation and 6 months for post-Open Notes documentation |
| Readability of AI-Modified vs. Original Clinical Notes Rated by Patients Description | This outcome assesses whether AI-modified clinical notes are rated as more readable than original notes. A total of 400 anonymized notes from the 12 months before the Open Notes rollout will be processed using large language models (e.g., ChatGPT, Neuroflash). Readability will be evaluated by patients using the Patient Education Materials Assessment Tool (PEMAT) and the Flesch-Kincaid Grade Level. | 27 months (including chatbot testing, AI modification of notes, and data collection) |
| Perceived Information Quality of AI-Modified vs. Original Clinical Notes Rated by Patients Description | This outcome assesses whether AI-modified clinical notes are rated as providing higher-quality information compared to original notes. A total of 400 anonymized notes from the 12 months before the Open Notes rollout will be processed using large language models (e.g., ChatGPT, Neuroflash). Perceived information quality will be measured by patients using selected items from the Health Information National Trends Survey (HINTS). | 27 months (including chatbot testing, AI modification of notes, and data collection) |
| Subjective Understanding of AI-Modified vs. Original Clinical Notes Rated by Patients Description | This outcome assesses whether AI-modified clinical notes are rated as more understandable than original notes. A total of 400 anonymized notes from the 12 months before the Open Notes rollout will be processed using large language models (e.g., ChatGPT, Neuroflash). Subjective understanding will be evaluated by patients using the Single Item Literacy Screener (SILS) and brief comprehension tasks. | 27 months (including chatbot testing, AI modification of notes, and data collection) |
| Technical and Organizational Requirements for Integrating Open Notes into the German Telematics Infrastructure (TI) and Electronic Health Record (ePA) | This module will produce technical and legal specifications for integrating Open Notes into the German national telematics infrastructure (TI), including the electronic patient record (ePA), the KIM communication system, and the TIM messaging service. Integration prototypes will be developed in three design thinking workshops (6-8 experts and 4 users each), ensuring HL7 FHIR® compliance. A Delphi-like process will generate and rate implementation concepts. Measured outcomes will include the number and type of integration use cases, HL7 FHIR® conformance levels, and expert consensus scores for each proposed concept. All results will align with existing interoperability and legal standards. | Up to 23 months |
| From baseline to follow-up, up to 24 months per participant in the intervention group |
| Change in Self-Efficacy Measured by the General Self-Efficacy Scale (GSE) | Assesses perceived self-efficacy using the General Self-Efficacy Scale, a 10-item measure scored from 1 to 4. Total scores range from 10 to 40, with higher scores indicating stronger self-efficacy expectations. Data will be collected via the PEPPPSY portal before and after first access to Open Notes. Analyses include intra-individual and between-group comparisons. Outcome modeling will account for usage frequency. | From baseline to follow-up, up to 24 months per participant in the intervention group |
| Change in Perceived Disease Management Measured by the EFK-HPC (Effektivität der Krankheitsbewältigung - Hochproblematische Chronizität) | Assesses coping strategies in patients with chronic illness using the EFK-HPC scale, a validated instrument designed to measure the effectiveness of coping with difficult chronic conditions. Items are rated on Likert scales; higher scores reflect better disease management. Data will be collected via the PEPPPSY portal pre- and post-intervention. Analyses include intra-individual and between-group comparisons; outcome modeling includes usage frequency. | From baseline to follow-up, up to 24 months per participant in the intervention group |
| Change in Patient Satisfaction Measured by the ZUF-8 Questionnaire | Assesses patient satisfaction using the ZUF-8, a validated short version of the Client Satisfaction Questionnaire (CSQ-8), with 8 items rated on a 4-point scale. Higher total scores indicate greater satisfaction with care. Data will be collected via the PEPPPSY portal before and after access to Open Notes. Analyses include intra-individual comparisons and group-level modeling that incorporates usage frequency. | From baseline to follow-up, up to 24 months per participant in the intervention group |
| Clarity, Accuracy, and Appropriateness of AI-Modified vs. Original Clinical Notes Rated by Health Professionals | This outcome assesses whether AI-modified clinical notes are rated as clearer, more accurate, and more appropriate than original notes. A total of 400 anonymized notes from the 12 months before the Open Notes rollout will be processed using large language models (e.g., ChatGPT, Neuroflash). Health care professionals will evaluate the notes using a modified DISCERN Instrument and structured evaluation forms. | 27 months (including chatbot testing, AI modification of notes, and data collection) |
| Julian Schwarz, MD, Specialist in Psychiatry |
| Medical School Brandenburg |
| Principal Investigator |
| Ute Karbach, Priv.-Doz. Dr. rer. pol. | University of Cologne | Principal Investigator |
| Background | Wurster, Florian, Eva Meier-Diedrich, I. Demirer, Catherine DesRoches, Nina Goldberg, Maria Hägglund, C. Herrmann, U. Karbach, A. Purohit, T. Schrader, J. Schwarz, 2024. "Online-Zugang Zu Klinischen Behandlungsnotizen Für Ambulant Versorgte Patienten (Studienprotokoll Zur OpenNOTES-Studie)." Nervenheilkunde, 43(12), 714-719. |