This descriptive feasibility study applied Work Domain Analysis (WDA) to examine ICU medication administration. We integrated medication-safety policy documents with interview data from three ICU nurses to develop an abstraction hierarchy. The analysis represents medication administration as a sociotechnical work domain and illustrates how adaptive strategies may emerge under system constraints and competing demands. These preliminary findings suggest that WDA is useful for contextualizing adaptive nursing behaviors as responses to domain constraints. The approach may support future work on risk-relevant adaptations and system-level intervention targets.

BackgroundChildren undergoing cancer treatment experience a range of treatment-related toxicities that significantly affect quality of life and adherence to therapy. Current methods for symptom reporting rely heavily on clinician interpretation of caregiver or child verbal reports, which can result in incomplete or inaccurate records. The Pediatric Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (Pediatric PRO-CTCAE; National Cancer Institute) provides a validated mechanism for direct symptom reporting by children and caregivers, yet its traditional administration and preselection of questions limit the breadth of symptom capture. ObjectiveThis research aimed to co-design and conduct formative usability testing of the Smart Pediatric Oncology Tracker of Symptoms (SPOTS), a novel, web-based interface for the Pediatric PRO-CTCAE to allow children with cancer and their caregivers to comprehensively report symptoms. MethodsThe research comprised 2 sequential phases: co-design and usability testing. Guided by child-computer interaction theory and participatory design methods, child-caregiver dyads collaborated with the research team to iteratively design and refine the SPOTS prototype. Nine participant dyads engaged in up to 3 co-design sessions that informed system features, layout, and content. During the usability phase, 12 additional dyads (6 with children aged 7-12 years and 6 with adolescents aged 13-17 years, each with a caregiver) completed structured usability tasks using the SPOTS prototype. Task completion, pathway efficiency, and user feedback were recorded through screen capture, field notes, and think-aloud protocols. Quantitative data were analyzed descriptively, and qualitative feedback was analyzed thematically. ResultsSPOTS was described by users as “very clear” and “easy to navigate.” Participants valued the visual design, the use of a customizable character, and the opportunity for children to report symptoms independently. Key usability challenges included confusing terminology, navigation redundancy, and visual complexities. Quantitative task analyses indicated that while most structured tasks were completed successfully, many required excess steps or assistance. When not directed to use a specific screen, participants’ symptom reporting methods varied, with caregivers and adolescents preferring the Body Parts Screen and younger children favoring the Search Screen. ConclusionsThe formative development of SPOTS demonstrates the feasibility and value of co-designing pediatric health technologies directly with children and caregivers. SPOTS has the potential to enhance the implementation of the Pediatric PRO-CTCAE by offering an engaging, child-friendly digital format that facilitates more direct symptom reporting. Future work will include a pilot study to further assess real-world usability, the quality of symptom capture (ie, completeness and accuracy), and integration with clinical workflows.

Remote patient monitoring (RPM) for hypertension management has become increasingly popular, demonstrating benefits for both clinics and patients. However, patient engagement in self-measured blood pressure (SMBP) monitoring remains low despite healthcare providers’ efforts. This study aimed to assess adherence and acceptance of RPM for SMBP among Texas Federally Qualified Health Center patients. Participants enrolled in the SMBP monitoring program were recruited at three health centers. Data was collected from patient health records, and a questionnaire assessed hypertension self-management, technology acceptance, intentions, and attitudes toward SMBP. Thematic analysis was conducted for open-ended responses that followed each item, and multivariate linear regression assessed associations between psychosocial factors and SMBP use. The sample (n = 47) was 64

Children undergoing cancer treatment experience a range of treatment-related toxicities that significantly affect quality of life and adherence to therapy. Current methods for symptom reporting rely heavily on clinician interpretation of caregiver or child verbal reports, which can result in incomplete or inaccurate records. The Pediatric Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (Pediatric PRO-CTCAE) provides a validated mechanism for direct symptom reporting by children and caregivers, yet its traditional administration and preselection of questions limit the breadth of symptom capture. This research aimed to co-design and conduct formative usability testing of the Smart Pediatric Oncology Tracker of Symptoms (SPOTS), a novel, web-based interface for the Pediatric PRO-CTCAE to allow children with cancer and their caregivers to comprehensively report symptoms. The research comprised two sequential phases: co-design and usability testing. Guided by child–computer interaction theory and participatory design methods, child–caregiver dyads collaborated with the research team to iteratively design and refine the SPOTS prototype. Nine participant dyads engaged in up to three co-design sessions that informed system features, layout, and content. During the usability phase, 12 additional dyads (6 with children aged 7–12 years and 6 with adolescents aged 13–17 years, each with a caregiver) completed structured usability tasks using the SPOTS prototype. Task completion, pathway efficiency, and user feedback were recorded through screen capture, field notes, and think-aloud protocols. Quantitative data were analyzed descriptively, and qualitative feedback was analyzed thematically. SPOTS was described by users as “very clear” and “easy to navigate”. Participants valued the visual design, the use of a customizable character, and the opportunity for children to report symptoms independently. Key usability challenges included confusing terminology, navigation redundancy, and visual complexities. Quantitative task analyses indicated that while most structured tasks were completed successfully, many required excess steps or assistance. When not directed to use a specific screen, participants’ symptom reporting methods varied, with caregivers and adolescents preferring the Body Parts Screen and younger children favoring the Search Screen. The formative development of SPOTS demonstrates the feasibility and value of co-designing pediatric health technologies directly with children and caregivers. SPOTS has the potential to enhance the implementation of the Pediatric PRO-CTCAE by offering an engaging, child-friendly digital format that facilitates more direct symptom reporting. Future work will include a pilot study to further assess real-world usability, the quality of symptom capture (i.e., completeness and accuracy), and integration with clinical workflows.

OBJECTIVE:Conduct a scoping review of research studies that describe rule-based clinical decision support (CDS) malfunctions. MATERIALS AND METHODS:In April 2022, we searched three bibliographic databases (MEDLINE, CINAHL, and Embase) for literature referencing CDS malfunctions. We coded the identified malfunctions according to an existing CDS malfunction taxonomy and added new categories for factors not already captured. We also extracted and summarized information related to the CDS system, such as architecture, data source, and data format. RESULTS:Twenty-eight articles met inclusion criteria, capturing 130 malfunctions. Architectures used included stand-alone systems (eg, web-based calculator), integrated systems (eg, best practices alerts), and service-oriented architectures (eg, distributed systems like SMART or CDS Hooks). No standards-based CDS malfunctions were identified. The "Cause" category of the original taxonomy includes three new types (organizational policy, hardware error, and data source) and two existing causes were expanded to include additional layers. Only 29 malfunctions (22%) described the potential impact of the malfunction on patient care. DISCUSSION:While a substantial amount of research on CDS exists, our review indicates there is a limited focus on CDS malfunctions, with even less attention on malfunctions associated with modern delivery architectures such as SMART and CDS Hooks. CONCLUSION:CDS malfunctions can and do occur across several different care delivery architectures. To account for advances in health information technology, existing taxonomies of CDS malfunctions must be continually updated. This will be especially important for service-oriented architectures, which connect several disparate systems, and are increasing in use.

Objective: High-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products leveraging advances in social computing, data science, and digital health. Methods: We describe three studies that leverage core methods from Digilego digital health development framework to (1) conduct large-scale social media analysis (n = 55 301 posts) to understand population-level patterns in women's needs, (2) architect a digital repository to enable women curate HRP related information, and (3) develop a digital platform to support PPD prevention. We applied a combination of qualitative coding, machine learning, theory-mapping, and programmatic implementation of theory-linked digital features. Further, we conducted preliminary testing of the resulting products for acceptance with sample of pregnant women for GDM/HTN information management (n = 10) and PPD prevention (n = 30). Results: Scalable social computing models using deep learning classifiers with reasonable accuracy have allowed us to capture and examine psychosociobehavioral drivers associated with HRPM. Our work resulted in two digital health solutions, MyPregnancyChart and MomMind are developed. Initial evaluation of both tools indicates positive acceptance from potential end users. Further evaluation with MomMind revealed statistically significant improvements (P < .05) in PPD recognition and knowledge on how to seek PPD information. Discussion: Digilego framework provides an integrative methodological lens to gain micro-macro perspective on women's needs, theory integration, engagement optimization, as well as subsequent feature and content engineering, which can be organized into core and specialized digital pathways for women engagement in disease management. Conclusion: Future works should focus on implementation and testing of digital solutions that facilitate women to capture, aggregate, preserve, and utilize, otherwise siloed, prenatal information artifacts for enhanced self-management of their high-risk conditions, ultimately leading to improved health outcomes.

Postpartum Depression (PPD) is the most common childbirth complication, with approximately 15% of postpartum women experiencing depression symptoms. Mobile applications have potential to expand delivery of mental health interventions. However, our understanding of how these tools engage women with PPD and facilitate positive behavioral changes is limited. In our paper, we analyze 15 commercial PPD applications to understand their role as facilitators of change, engagement, and sustained use. Applications reviewed contained an average of four theory-based behavioral change techniques, and highest patient engagement level reached was to empower patients through patient-generated data. Heuristic violations were identified in areas including user control and freedom, aesthetic and minimalist design, and help and documentation. An inverse correlation was found between the number of theory-based behavior change features and patient engagement. Findings suggest underserved populations may suffer further limitations accessing relevant health resources in the current application market.

Digital technologies offer many opportunities to improve mental healthcare management for women seeking pre- and-postnatal care. They provide a discrete, practical medium that is well-suited for the sensitive nature of mental health. Women who are more prone to experiencing peripartum depression (PPD), such as those of low-socioeconomic background or in high-risk pregnancies, can benefit the most from such technologies. However, current digital interventions directed towards this population provide suboptimal support, and their responsiveness to end user needs is quite limited. Our objective is to understand the digital terrain of information needs for low-socioeconomic status women with high-risk pregnancies, specifically within the management of their mental health. This qualitative study consists of semi-structured focus groups and interviews with a sample of nineteen patients. A total of eleven core themes emerged from participant comments. Resulting themes highlighted the need for digital technologies that promote personalized care, a sense of community, and improved provider communication.

Digital health technologies offer unique opportunities to improve health outcomes for mental health conditions such as peripartum depression (PPD), a disorder that affects approximately 10-15% of women in the U.S. every year. In this paper, we present the adaption of a digital technology development framework, Digilego, in the context of PPD. Methods include mapping of the Behavior Intervention Technology (BIT) model and the Patient Engagement Framework (PEF) to translate patient needs captured through focus groups. This informs formative development and implementation of digital health features for optimal patient engagement in PPD screening and management. Results show an array ofPPD-specific Digilego blocks ("My Diary", "Mom Talk", "My Care", "Library", "How am I doing today?"). Initial evaluation results from comparative market analysis indicate that our proposed platform offers advantageous technology aspects. Limitations and future work in areas of interdisciplinary care coordination and patient engagement optimization are discussed.

BackgroundDysfunctions in the renin-angiotensin system (RAS) seem to be involved in the pathophysiology of several mental illness, including schizophrenia and mood disorders. We carried out a cross-sectional study assessing the levels of RAS-related molecules among bipolar disorder (BD) patients compared to healthy controls.Methodsour sample consisted of 30 outpatients with BD type 1 (10 males, 20 females, age = 35.53 ± 10.59 years, 14 euthymic, 16 experiencing mood episodes) and 30 healthy controls (10 males, 20 females, age = 34.83 ± 11.49 years). Plasma levels of angiotensin-converting enzyme (ACE), angiotensin-converting enzyme 2 (ACE2), angiotensin-II (Ang II), and angiotensin (1–7) [Ang-(1–7)] were determined by ELISA.ResultsBD patients experiencing ongoing mood episodes had significantly lower ACE levels compared to controls (median: 459.00 vs. 514.10, p < 0.05). There was no association between the levels of these biomarkers and clinical parameters.ConclusionOur findings support the involvement of RAS dysfunction in the pathophysiology of BD. Considering the potential therapeutic implications linked to a better understanding of the role of RAS dysfunction in BD, studies allowing a better characterization of RAS-related molecules level and activity across different mood states are of high interest.

Objective This study demonstrates application of human factors methods for understanding causes for lack of timely follow-up of abnormal test results ("missed results") in outpatient settings. Methods We identified 30 cases of missed test results by querying electronic health record data, developed a critical decision method (CDM)-based interview guide to understand decision-making processes, and interviewed physicians who ordered these tests. We analyzed transcribed responses using a contextual inquiry (CI)-based methodology to identify contextual factors contributing to missed results. We then developed a CI-based flow model and conducted a fault tree analysis (FTA) to identify hierarchical relationships between factors that delayed action. Results The flow model highlighted barriers in information flow and decision making, and the hierarchical model identified relationships between contributing factors for delayed action. Key findings including underdeveloped methods to track follow-up, as well as mismatches, in communication channels, timeframes, and expectations between patients and physicians. Conclusion This case report illustrates how human factors-based approaches can enable analysis of contributing factors that lead to missed results, thus informing development of preventive strategies to address them.

BACKGROUND Peripartum depression has an incidence rate of approximately 11% among pregnant women and new mothers in the U.S, yet many cases go undiagnosed and untreated due to psychosocial and logistical barriers. The interdisciplinary nature of PPD management presents a challenging scenario for communication and coordination between care providers and patients. Digital technologies offer many opportunities to improve mental healthcare management for women seeking pre- and-postnatal care. They provide a discrete, practical medium that is well-suited for the sensitive nature of mental health. However, current digital interventions in this domain provide suboptimal support and their responsiveness to end user needs is quite limited. OBJECTIVE Our objective is to map the terrain of information needs of women seeking pre-and-postnatal care, specifically for the management of their mental health. Ultimately, we will apply the derived insights to inform our development efforts of a novel digital solution to be used in an academic clinical setting. METHODS This qualitative study consists of semi-structured focus group interviews with a total of nine patients receiving care at an academic Obstetrics and Gynecology clinic. Discussion questions covered topics such as smartphone use and pregnancy apps. A total of two sessions were conducted and audio recorded. Data from the recordings were analyzed through thematic analysis. Overarching themes and views from the sessions were annotated. A survey was also administered to assess participants’ information seeking behavior and use of popular applications. RESULTS Access to smartphones and data plans was not an issue for participants. All used at least one pregnancy app, and three used additional health apps such as pedometers. Easy access to informational and human resources such as evidenced-based pregnancy information, their healthcare providers, and their peers was common across participants. The sense of community offered in discussion boards was also important to participants. Lack of postpartum support and limited personalization of user experience in current digital tools were identified as gaps and barriers for management of PPD and other mental health disorders. Digital technology features recommended by participants were: 1) a journaling feature, 2) online library of keywords linking to credible information, 3) a responsive bi-directional communication pathway to interact with their clinical team, and 4) personalized support for second pregnancy, diabetes, and prior depressive episodes. CONCLUSIONS Users prefer digital technology systems that go beyond a generic approach. They also want these systems to be adaptable to their needs at the moment, whether it is consulting with their provider or simply seeking peer support. Future technologies aimed at maternal mental health should focus on the individual needs of patients, while at the same time offering them common digital tools such as online communities and easy access to reliable information. CLINICALTRIAL N/A

OBJECTIVE Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients' independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder. MATERIALS AND METHODS We propose a dual adversarial autoencoder (DAAE), which learns set-valued sequences of medical entities, by combining a recurrent autoencoder with 2 generative adversarial networks (GANs). DAAE improves the mode coverage and quality of generated sequences by adversarially learning both the continuous latent distribution and the discrete data distribution. Using the MIMIC-III (Medical Information Mart for Intensive Care-III) and UT Physicians clinical databases, we evaluated the performances of DAAE in terms of predictive modeling, plausibility, and privacy preservation. RESULTS Our generated sequences of EHRs showed the comparable performances to real data for a predictive modeling task, and achieved the best score in plausibility evaluation conducted by medical experts among all baseline models. In addition, differentially private optimization of our model enables to generate synthetic sequences without increasing the privacy leakage of patients' data. CONCLUSIONS DAAE can effectively synthesize sequential EHRs by addressing its main challenges: the synthetic records should be realistic enough not to be distinguished from the real records, and they should cover all the training patients to reproduce the performance of specific downstream tasks.

Objective: Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients’ independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder. Materials and Methods: We propose a dual adversarial autoencoder (DAAE), which learns set-valued sequences of medical entities, by combining a recurrent autoencoder with 2 generative adversarial networks (GANs). DAAE improves the mode coverage and quality of generated sequences by adversarially learning both the continuous latent distribution and the discrete data distribution. Using the MIMIC-III (Medical Information Mart for Intensive Care-III) and UT Physicians clinical databases, we evaluated the performances of DAAE in terms of predictive modeling, plausibility, and privacy preservation. Results: Our generated sequences of EHRs showed the comparable performances to real data for a predictive modeling task, and achieved the best score in plausibility evaluation conducted by medical experts among all baseline models. In addition, differentially private optimization of our model enables to generate synthetic sequences without increasing the privacy leakage of patients’ data. Conclusions: DAAE can effectively synthesize sequential EHRs by addressing its main challenges: the synthetic records should be realistic enough not to be distinguished from the real records, and they should cover all the training patients to reproduce the performance of specific downstream tasks.

INTRODUCTION: Persistent contamination of elevator mechanism despite reprocessing has resulted in outbreaks of infection with multidrug-resistant organisms following Endoscopic Retrograde Cholangiopancreatography (ERCP). Novel duodenoscope with disposable elevator cap (DEC) might reduce these infections. We tested performance of DEC-duodenoscope in patients undergoing ERCP. METHODS: We conducted a prospective case-series study of outcomes of ERCP using DEC duodenoscope at two academic medical centers. Six endoscopists (experience 1 to 10+ years) and nurse/technicians participated in the study. Outcomes analyzed included evaluation of pre-procedure set up, post procedure reprocessing by nurse/technicians. ERCP outcomes included successful completion of ERCP, endoscopist rating on ERCP performance characteristics as well as ERCP maneuvers, and adverse events. RESULTS: This is an interim analysis of 108 consecutive ERCPs (Female 66%, 71/108) performed during the study period of May and June 2020. Most common indication was choledocholithiasis (60%, 65/108) and a majority of patients had native papilla (65%, 70/108). 32% of procedures were performed by fellows, while 68% of procedures were performed by attending physicians. Majority of ERCPs were of ASGE ERCP complexity Grade 2 (73%, 79/108). The mean cannulation time was 5.85 mins (SD: ± 7.97 mins) while the mean time to complete the procedure was 26 mins (SD: ± 17.66 mins). All ERCPs were performed under propofol guided deep sedation or general endotracheal anesthesia. All ERCPs were successfully completed without need to change scope . In one case, given inability to pass the instrument via the elevator channel, ERCP scope was removed, same cap was reattached, and then procedure could be successfully completed. Overall performance rating for DEC duodenoscope was 99%-100% in 17 ERCP performance characteristics and 100% in 10 ERCP maneuvers (Table 1). When evaluated by nurse or technician, overall performance rating of DEC duodenoscope was 97%-99% in various pre-procedure, intraprocedural and post-procedure tasks (Table 2). CONCLUSION: We conclude that most endoscopists with varying level of experience, could easily complete various complex ERCP tasks with DEC duodenoscope. This innovative design has the potential to reduce ERCP related infectious outbreak.Table 1.: Rating on ERCP Performance Characteristics and ERCP ManeuversTable 2.: Rating by nurse of technician for pre-procedure set up, intraprocedural performance and post-procedure reprocessing steps

The negative effects of long-term stress on health outcomes are well-documented. Emerging technologies that harness mobile technologies have been linked to positive effects on stress management. However, the ways in which existing inter- and intrapersonal theories of behavior change are integrated into development processes of these mHealth technologies for stress coping are limited. In this paper, we present a novel theory-driven approach to develop and implement a sustainable mobile application for stress education and management. Specifically, we integrate the taxonomy of Behavior Change Techniques and user engagement framework to model and adapt theory-driven techniques in the context of mobile technologies. A total of 12 behavior change techniques were incorporated into our mobile application. Initial user evaluation and usability testing was conducted. Results indicate heuristic modifications could improve overall delivery of content, and potential user satisfaction is likely. We conclude that this novel approach may have implications well beyond stress management.

Objective: Despite ongoing efforts to improve reliability of the total testing process (TTP), breakdowns continue to occur resulting in diagnostic delays and suboptimal patient outcomes. We performed an exploratory study to identify factors that impact TTP reliability in electronic health record (EHR)-enabled care. Materials and methods: We interviewed experts at three large EHR-enabled health care organizations and identified all TTP steps performed from clinician test ordering to result communication to patients. Findings from all sites were combined to develop a detailed process map of known TTP activities. We additionally asked experts about factors that positively or negatively impacted TTP reliability at each step. We describe the specific TTP steps identified and associated barriers and facilitators to TTP reliability. Results: We interviewed 39 experts involved in or overseeing the TTP. Most TTP activities identified were similar across sites, but we found significant differences with test order transmission to diagnostic services and relay of results back to clinicians and patients. Twenty-five unique barriers were identified related to technology and EHR usability issues, time and resource constraints, suboptimal clinic workflows, patient-related factors, information access limitations, and insufficient clinician training. Twenty-four unique facilitators were identified related to personnel training, workflow optimization and standardization, helpful EHR features, and improved electronic communication between clinics and diagnostic services. Discussion: Barriers related to EHR usability and with communication between clinicians and diagnostic services increase TTP vulnerability and should be targeted by future efforts to improve process reliability. Several facilitators identified in the study could inform future strategies and solutions to improve TTP reliability.

s: 1. Menon S, Singh H, Giardina T, Rayburn W L, Davis B P, Russo E M, Sittig D F. Role of Safety Huddles to Promote Learning about Electronic Health Record-related Safety Risks. 2015 AcademyHealth Annual Research Meeting, Minneapolis, MN. June 14-15, 2015. 2. Murphy DR, Smith, MW, Russo E, Sittig DF, Singh H. Sociotechnical Analysis of Test Result Communication in Three EHR-Based Clinical Settings. Agency for Healthcare Research & Quality Annual Conference. Washington DC. October 4-6, 2015. 3. Russo E, Sittig DF, Murphy DR, Smith MW, Singh H. Challenges in Patient Safety Improvement Research in the Era of Electronic Health Records: Lessons from an AHRQ Funded Multi-Site Study. Agency for Healthcare Research & Quality Annual Conference. Washington DC. October 4-6, 2015. 4. Menon S, Singh H, Giardina TD, Rayburn WL, Davis BP, Russo E, Sittig DF. Exploring the Use of Safety Huddles as a Strategy to Promote Learning about Electronic Health Record-Related Safety Risks. Agency for Healthcare Research & Quality Annual Conference. Washington DC. October 4-6, 2015. 5. Murphy DR, Smith, MW, Sittig DF, Russo E, Singh H, Variation in EHR Implementations and the Impact on Safety of Test Result Follow-up. American Medical Informatics 2015 Association Annual Conference. San Francisco, CA. November 13-17, 2015. 6. Roosan D, Bhise V, Schwartz-Micheaux J, Russo E, Murphy DR, Sittig DF, Singh H. Understanding Delays in Abnormal Test Result Follow-Up Using Electronic Health Records In Outpatient Primary Care Settings. American Medical Informatics Association 2016 Annual Conference. Chicago, IL. November 12-16, 2016.

To present user needs and usability evaluations of DataMed, a Data Discovery Index (DDI) that allows searching for biomedical data from multiple sources. We conducted 2 phases of user studies. Phase 1 was a user needs analysis conducted before the development of DataMed, consisting of interviews with researchers. Phase 2 involved iterative usability evaluations of DataMed prototypes. We analyzed data qualitatively to document researchers' information and user interface needs. Biomedical researchers' information needs in data discovery are complex, multidimensional, and shaped by their context, domain knowledge, and technical experience. User needs analyses validate the need for a DDI, while usability evaluations of DataMed show that even though aggregating metadata into a common search engine and applying traditional information retrieval tools are promising first steps, there remain challenges for DataMed due to incomplete metadata and the complexity of data discovery. Biomedical data poses distinct problems for search when compared to websites or publications. Making data available is not enough to facilitate biomedical data discovery: new retrieval techniques and user interfaces are necessary for dataset exploration. Consistent, complete, and high-quality metadata are vital to enable this process. While available data and researchers' information needs are complex and heterogeneous, a successful DDI must meet those needs and fit into the processes of biomedical researchers. Research directions include formalizing researchers' information needs, standardizing overviews of data to facilitate relevance judgments, implementing user interfaces for concept-based searching, and developing evaluation methods for open-ended discovery systems such as DDIs.