A pilot acceptability evaluation of MomMind: A digital health intervention for Peripartum Depression prevention and management focused on health disparities.

Health disparities cause significant strain on the wellbeing of individuals and society. In this study, we focus on the health disparities present in the condition of Peripartum Depression (PPD), a significant public health issue. While PPD can be managed through therapy and medication, many women do not receive adequate PPD treatment due to issues of social stigma and limited access to healthcare resources. Digital health technologies can offer practical tools for PPD management. However, current solutions do not integrate behavior theory and are rarely responsive to the transient information needs stemming from women's unique sociodemographic, clinical and psychosocial profiles. We describe a pilot acceptability evaluation of MomMind, a health-disparities focused digital health intervention for the prevention and management of PPD. A crucial MomMind advantage is its basis on behavior change theory and patient engagement as enabled by the Digilego digital health framework. Following an internal usability evaluation, MomMind was evaluated by patients through cross-sectional acceptability surveys, pre-and-post PPD health literacy surveys, and interviews. Survey respondents included n = 30 peripartum women, of whom n = 16 (53.3%) were Hispanic and n = 17 (56.7%) of low-income. Survey results show that 96.6% of participants (n = 29) approved and welcomed MomMind, and 90% (n = 27) found MomMind to be an appealing intervention. Additionally, significant improvements (p< = 0.05) were observed in participants' PPD health literacy, specifically their ability to recognize PPD symptoms and knowledge of how to seek PPD information. Interview main themes include MomMind's straightforward design and influence of others (family members, providers) on use of technology. Results suggest that enhancement of a digital health framework with health literacy theory can support production of digital health solutions acceptable to vulnerable populations. This study incorporates existing theories from different disciplines into a unified approach for mitigating health disparities, and produced a novel solution for promotion of health in a vulnerable population.

Digital health technologies for high-risk pregnancy management: three case studies using Digilego framework.

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.

Community scientist program provides bi-directional communication and co-learning between researchers and community members.

Community involvement in research is key to translating science into practice, and new approaches to engaging community members in research design and implementation are needed. The Community Scientist Program, established at the MD Anderson Cancer Center in Houston in 2018 and expanded to two other Texas institutions in 2021, provides researchers with rapid feedback from community members on study feasibility and design, cultural appropriateness, participant recruitment, and research implementation. This paper aims to describe the Community Scientist Program and assess Community Scientists' and researchers' satisfaction with the program. We present the analysis of the data collected from 116 Community Scientists and 64 researchers who attended 100 feedback sessions, across three regions of Texas including Northeast Texas, Houston, and Rio Grande Valley between June 2018 and December 2022. Community Scientists stated that the feedback sessions increased their knowledge and changed their perception of research. All researchers (100%) were satisfied with the feedback and reported that it influenced their current and future research methods. Our evaluation demonstrates that the key features of the Community Scientist Program such as follow-up evaluations, effective bi-directional communication, and fair compensation transform how research is conducted and contribute to reducing health disparities.

Revealing chronic disease progression patterns using Gaussian process for stage inference.

OBJECTIVE: The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development. MATERIALS AND METHODS: We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), bipolar disorder (BP) in the Adolescent Brain Cognitive Development Study (ABCD), and hepatocellular carcinoma (HCC) in the UTHealth and The Cancer Genome Atlas (TCGA). RESULTS: First, GPSI identified two subgroups of OA based on image features, where these subgroups corresponded to different genotypes, indicating the bone-remodeling and overweight-related pathways. Second, GPSI differentiated BP into two distinct developmental patterns and defined the contribution of specific brain region atrophy from early to advanced disease stages, demonstrating the ability of the GPSI to identify diagnostic subgroups. Third, HCC progression patterns were well reproduced in the two independent UTHealth and TCGA datasets. CONCLUSION: Our study demonstrated that an unsupervised approach can disentangle temporal and phenotypic heterogeneity and identify population subgroups with common patterns of disease progression. Based on the differences in these features across stages, physicians can better tailor treatment plans and medications to individual patients.

Digital health technologies for peripartum depression management among low-socioeconomic populations: perspectives from patients, providers, and social media channels.

BACKGROUND: Peripartum Depression (PPD) affects approximately 10-15% of perinatal women in the U.S., with those of low socioeconomic status (low-SES) more likely to develop symptoms. Multilevel treatment barriers including social stigma and not having appropriate access to mental health resources have played a major role in PPD-related disparities. Emerging advances in digital technologies and analytics provide opportunities to identify and address access barriers, knowledge gaps, and engagement issues. However, most market solutions for PPD prevention and management are produced generically without considering the specialized needs of low-SES populations. In this study, we examine and portray the information and technology needs of low-SES women by considering their unique perspectives and providers' current experiences. We supplement our understanding of women's needs by harvesting online social discourse in PPD-related forums, which we identify as valuable information resources among these populations. METHODS: We conducted (a) 2 focus groups (n = 9), (b) semi-structured interviews with care providers (n = 9) and low SES women (n = 10), and (c) secondary analysis of online messages (n = 1,424). Qualitative data were inductively analyzed using a grounded theory approach. RESULTS: A total of 134 open concepts resulted from patient interviews, 185 from provider interviews, and 106 from focus groups. These revealed six core themes for PPD management, including "Use of Technology/Features", "Access to Care", and "Pregnancy Education". Our social media analysis revealed six PPD topics of importance in online messages, including "Physical and Mental Health" (n = 725 messages), and "Social Support" (n = 674). CONCLUSION: Our data triangulation allowed us to analyze PPD information and technology needs at different levels of granularity. Differences between patients and providers included a focus from providers on needing better support from administrative staff, as well as better PPD clinical decision support. Our results can inform future research and development efforts to address PPD health disparities.

Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.

BACKGROUND: Predicting outcomes of patients with COVID-19 at an early stage is crucial for optimised clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, because of their requirements for extensive data preprocessing and feature engineering, they have not been validated or implemented outside of their original study site. Therefore, we aimed to develop accurate and transferrable predictive models of outcomes on hospital admission for patients with COVID-19. METHODS: In this study, we developed recurrent neural network-based models (CovRNN) to predict the outcomes of patients with COVID-19 by use of available electronic health record data on admission to hospital, without the need for specific feature selection or missing data imputation. CovRNN was designed to predict three outcomes: in-hospital mortality, need for mechanical ventilation, and prolonged hospital stay (>7 days). For in-hospital mortality and mechanical ventilation, CovRNN produced time-to-event risk scores (survival prediction; evaluated by the concordance index) and all-time risk scores (binary prediction; area under the receiver operating characteristic curve [AUROC] was the main metric); we only trained a binary classification model for prolonged hospital stay. For binary classification tasks, we compared CovRNN against traditional machine learning algorithms: logistic regression and light gradient boost machine. Our models were trained and validated on the heterogeneous, deidentified data of 247 960 patients with COVID-19 from 87 US health-care systems derived from the Cerner Real-World COVID-19 Q3 Dataset up to September 2020. We held out the data of 4175 patients from two hospitals for external validation. The remaining 243 785 patients from the 85 health systems were grouped into training (n=170 626), validation (n=24 378), and multi-hospital test (n=48 781) sets. Model performance was evaluated in the multi-hospital test set. The transferability of CovRNN was externally validated by use of deidentified data from 36 140 patients derived from the US-based Optum deidentified COVID-19 electronic health record dataset (version 1015; from January, 2007, to Oct 15, 2020). Exact dates of data extraction were masked by the databases to ensure patient data safety. FINDINGS: CovRNN binary models achieved AUROCs of 93·0% (95% CI 92·6-93·4) for the prediction of in-hospital mortality, 92·9% (92·6-93·2) for the prediction of mechanical ventilation, and 86·5% (86·2-86·9) for the prediction of a prolonged hospital stay, outperforming light gradient boost machine and logistic regression algorithms. External validation confirmed AUROCs in similar ranges (91·3-97·0% for in-hospital mortality prediction, 91·5-96·0% for the prediction of mechanical ventilation, and 81·0-88·3% for the prediction of prolonged hospital stay). For survival prediction, CovRNN achieved a concordance index of 86·0% (95% CI 85·1-86·9) for in-hospital mortality and 92·6% (92·2-93·0) for mechanical ventilation. INTERPRETATION: Trained on a large, heterogeneous, real-world dataset, our CovRNN models showed high prediction accuracy and transferability through consistently good performances on multiple external datasets. Our results show the feasibility of a COVID-19 predictive model that delivers high accuracy without the need for complex feature engineering. FUNDING: Cancer Prevention and Research Institute of Texas.

Training Leaders in Health Informatics.

Despite the widespread adoption of electronic health records (EHRs) in the U.S. over the past decade, significant improvements, especially in patient safety, have yet to be realized. This finding, along with health informatics workforce data and an identified gap in the offerings of an educational program, led to a proposed professional doctorate in health informatics. Developed via stakeholder focus groups, the program was approved by the public university system, the state-level educational authority, and the regional accreditation body, with final approval in July 2018. Unique features of the program include a prolonged practice project demonstrating a return on investment, as well as online and face-to-face delivery components. This program aims to develop evidence-based professionals who improve the health of people and populations through the application of health informatics. Applications and interest in the first class are high.

Renal Replacement Therapy in Severe Burns: A Multicenter Observational Study.

Acute kidney injury (AKI) after severe burns is historically associated with a high mortality. Over the past two decades, various modes of renal replacement therapy (RRT) have been used in this population. The purpose of this multicenter study was to evaluate demographic, treatment, and outcomes data among severe burn patients treated with RRT collectively at various burn centers around the United States. After institutional review board approval, a multicenter observational study was conducted. All adult patients aged 18 or older, admitted with severe burns who were placed on RRT for acute indications but not randomized into a concurrently enrolling interventional trial, were included. Across eight participating burn centers, 171 subjects were enrolled during a 4-year period. Complete data were available in 170 subjects with a mean age of 51 ± 17, percent total body surface area burn of 38 ± 26% and injury severity score of 27 ± 21. Eighty percent of subjects were male and 34% were diagnosed with smoke inhalation injury. The preferred mode of therapy was continuous venovenous hemofiltration at a mean delivered dose of 37 ± 19 (ml/kg/hour) and a treatment duration of 13 ± 24 days. Overall, in hospital, mortality was 50%. Among survivors, 21% required RRT on discharge from the hospital while 9% continued to require RRT 6 months after discharge. This is the first multicenter cohort of burn patients who underwent RRT reported to date. Overall mortality is comparable to other critically ill populations who undergo RRT. Most patients who survive to discharge eventually recover renal function.

An Outpatient Performance Improvement Project: A Baseline Assessment of Adherence to Pain Reassessment Standards.

BACKGROUND: This performance improvement (PI) project was conducted to recommend improvements for pain reassessment workflow and policies at a large military primary care clinic. The Joint Commission survey identified inconsistent pain reassessment practices at the facility in 2012. A review of the literature reveals that pain reassessment procedures can be affected by unclear organizational policies, poorly designed documentation procedures, and redundant or inefficient workflow practices. This PI project was designed to assess pain reassessment compliance rates, associated documentation, and clinic workflow, and to identify opportunities for improvement. METHODS: Pain reassessment compliance was evaluated using an Electronic Medical Record (EMR) query for patients treated between February 1 and May 30, 2013, who received Toradol at a large military outpatient clinic (n = 151). In addition, observations of clinic workflow were conducted using tracer methodology as recommended by The Joint Commission to track a convenience sample of 12 patients moving through clinic care processes. Pain reassessment documentation and workflow procedures were then evaluated using the Situation Awareness (SA) framework, which is an approach used to evaluate operational implications of factors affecting staff decisions and performance (e.g., stress and workload, interface design, automation, complexity of workflow, staff abilities and training, goals and expectations). RESULTS: The EMR review revealed compliance rates greater than 90% for all pain reassessment requirements with the exception of the maximum 30-minute interval between initial and follow-up pain assessment required by clinic policy, which had a compliance rate of 38%. Pain reassessments were documented to occur at a mean time of 48.25 minutes after initial assessment. During the tracer, none of the 12 patient encounters was fully compliant with clinic policies. An analysis of clinic workflow using the SA framework revealed that the SA of clinic staff was impacted by a lack of standardized procedures and heavy reliance on staff memory. DISCUSSION: Recommendations for improvement included possible extension of the 30-minute time requirement, development of a template for pain reassessment documentation in the EMR, standardizing hand off and admission/discharge processes, and designing an electronic or manual dashboard to indicate pain reassessment times. Future PI projects in other military clinics would benefit from use of the SA perspective to review clinic policies, EMR documentation, and workflow analysis. Further analysis will be needed to evaluate the impact of these improvements.

High-volume hemofiltration in adult burn patients with septic shock and acute kidney injury: a multicenter randomized controlled trial.

BACKGROUND: Sepsis and septic shock occur commonly in severe burns. Acute kidney injury (AKI) is also common and often results as a consequence of sepsis. Mortality is unacceptably high in burn patients who develop AKI requiring renal replacement therapy and is presumed to be even higher when combined with septic shock. We hypothesized that high-volume hemofiltration (HVHF) as a blood purification technique would be beneficial in this population. METHODS: We conducted a multicenter, prospective, randomized, controlled clinical trial to evaluate the impact of HVHF on the hemodynamic profile of burn patients with septic shock and AKI involving seven burn centers in the United States. Subjects randomized to the HVHF were prescribed a dose of 70 ml/kg/hour for 48 hours while control subjects were managed in standard fashion in accordance with local practices. RESULTS: During a 4-year period, a total of nine subjects were enrolled for the intervention during the ramp-in phase and 28 subjects were randomized, 14 each into the control and HVHF arms respectively. The study was terminated due to slow enrollment. Ramp-in subjects were included along with those randomized in the final analysis. Our primary endpoint, the vasopressor dependency index, decreased significantly at 48 hours compared to baseline in the HVHF group (p = 0.007) while it remained no different in the control arm. At 14 days, the multiple organ dysfunction syndrome score decreased significantly in the HVHF group when compared to the day of treatment initiation (p = 0.02). No changes in inflammatory markers were detected during the 48-hour intervention period. No significant difference in survival was detected. No differences in adverse events were noted between the groups. CONCLUSIONS: HVHF was effective in reversing shock and improving organ function in burn patients with septic shock and AKI, and appears safe. Whether reversal of shock in these patients can improve survival is yet to be determined. TRIAL REGISTRATION: Clinicaltrials.gov NCT01213914 . Registered 30 September 2010.

A novel computational model to probe visual search deficits during motor performance.

Successful execution of many motor skills relies on well-organized visual search (voluntary eye movements that actively scan the environment for task-relevant information). Although impairments of visual search that result from brain injuries are linked to diminished motor performance, the neural processes that guide visual search within this context remain largely unknown. The first objective of this study was to examine how visual search in healthy adults and stroke survivors is used to guide hand movements during the Trail Making Test (TMT), a neuropsychological task that is a strong predictor of visuomotor and cognitive deficits. Our second objective was to develop a novel computational model to investigate combinatorial interactions between three underlying processes of visual search (spatial planning, working memory, and peripheral visual processing). We predicted that stroke survivors would exhibit deficits in integrating the three underlying processes, resulting in deteriorated overall task performance. We found that normal TMT performance is associated with patterns of visual search that primarily rely on spatial planning and/or working memory (but not peripheral visual processing). Our computational model suggested that abnormal TMT performance following stroke is associated with impairments of visual search that are characterized by deficits integrating spatial planning and working memory. This innovative methodology provides a novel framework for studying how the neural processes underlying visual search interact combinatorially to guide motor performance. NEW & NOTEWORTHY: Visual search has traditionally been studied in cognitive and perceptual paradigms, but little is known about how it contributes to visuomotor performance. We have developed a novel computational model to examine how three underlying processes of visual search (spatial planning, working memory, and peripheral visual processing) contribute to visual search during a visuomotor task. We show that deficits integrating spatial planning and working memory underlie abnormal performance in stroke survivors with frontoparietal damage.

Pharmacokinetics of Imipenem/Cilastatin Burn Intensive Care Unit Patients Undergoing High-Dose Continuous Venovenous Hemofiltration.

STUDY OBJECTIVE: High-dose continuous venovenous hemofiltration (CVVH) is a continuous renal replacement therapy (CRRT) used frequently in patients with burns. However, antibiotic dosing is based on inference from studies assessing substantially different methods of CRRT. To address this knowledge gap for imipenem/cilastatin (I/C), we evaluated the systemic and extracorporeal clearances (CLs) of I/C in patients with burns undergoing high-dose CVVH. DESIGN: Prospective clinical pharmacokinetic study. PATIENTS: Ten adult patients with burns receiving I/C for a documented infection and requiring high-dose CVVH were studied. METHODS: Blood and effluent samples for analysis of I/C concentrations were collected for up to 6 hours after the I/C infusion for calculation of I/C total CL (CLTotal ), CL by CVVH (CLHF ), half-life during CVVH, volume of distribution at steady state (Vdss ), and the percentage of drug eliminated by CVVH. RESULTS: In this patient sample, the mean age was 50 ± 17 years, total body surface area burns was 23 ± 27%, and 80% were male. Nine patients were treated with high-dose CVVH for acute kidney injury and one patient for sepsis. The mean delivered CVVH dose was 52 ± 14 ml/kg/hour (range 32-74 ml/kg/hr). The imipenem CLHF was 3.27 ± 0.48 L/hour, which accounted for 23 ± 4% of the CLTotal (14.74 ± 4.75 L/hr). Cilastatin CLHF was 1.98 ± 0.56 L/hour, which accounted for 45 ± 19% of the CLTotal (5.16 + 2.44 L/hr). The imipenem and cilastatin half-lives were 1.77 ± 0.38 hours and 4.21 ± 2.31 hours, respectively. Imipenem and cilastatin Vdss were 35.1 ± 10.3 and 32.8 ± 13.8 L, respectively. CONCLUSION: Efficient removal of I/C by high-dose CVVH, a high overall clearance, and a high volume of distribution in burn intensive care unit patients undergoing this CRRT method warrant aggressive dosing to treat serious infections effectively depending on the infection site and/or pathogen.

Breakages using a unilateral fixed functional appliance: a case report using The Forsus Fatigue Resistant Device.

Fixed functional appliances are designed to provide a simple non-compliant solution to orthodontic Class II treatment. Molar correction can be achieved very quickly using these appliances, but the clinician should be wary of unexpected breakages. This case report documents such an occurrence using a unilateral fixed functional appliance.

Interprofessional relationships and collaborative working: encouraging reflective practice.

A challenge for those involved in the education and professional development of health and social care practitioners is to find ways of encouraging and enabling them to critically reflect upon complex collaborative situations and consider how they might improve interprofessional relationships. To meet this challenge, we piloted and developed a reflective exercise derived from methods used in personal construct psychology (Hargreaves,1979; Salmon,1994), which has proved to be useful in three overlapping areas; research, professional development, and classroom teaching. To illustrate the technique, this paper presents a case study of one district nurse who used the method to help her examine complex interprofessional relationships when providing long-term community care. The reflective technique (which uses arrow-shaped cards displayed on large visual layouts) was found to provide a rich description of the individual's relationships. By employing the visual displays the district nurse was able to explore the meanings of professional identity and roles in terms of professional relationships, and to consider her intentions and actions within a complex multidisciplinary situation.

Professional identities and interprofessional relations: evaluation of collaborative community schemes.

Despite the widespread support for integrated community care in the U.K., interdisciplinary working between health and social service staff remains complex and tentative. This paper represents the findings of an evaluative research study of two collaborative community nursing schemes at differing stages of project development within West Yorkshire, England. The studies set out to explore in depth the ways social and health care professionals construe their identity and relationships within the changing context of collaborative projects. The research adopted a constructivist phenomenological approach, conducting focus groups with social workers and district nurses; supplemented by in-depth individual interviews. Using template analysis enabled the researchers to explore themes and issues generated from the groups and individuals. A number of areas associated with professional identity and the development of roles in response to changing situations were identified as pertinent including; role ambiguity, role erosion and extension. In cases where joint working required a reconstructing of professional identity, individuals were constrained by their personal meanings, organizational arrangements and public perceptions. Implications for research and practice are discussed.