Determinants of Nutrition Agenda Setting in the Context of the Double Burden of Malnutrition in Tamil Nadu, India

The double burden of malnutrition is increasing in low- and middleincome countries, with economic, social, and health consequences. Policies and programs to address malnutrition at the national and subnational levels reflect the priorities and framing of the problem by the stakeholder community. Previous studies have examined if and how nutrition-related NCDs have been included into national nutrition policy agendas that have historically focused on reduction of undernutrition, but little is known about if or how this process occurs at the subnational level where policies are translated and implemented according to local contexts, priorities, and frames of nutrition-related NCDs. We aimed to improve understanding of the determinants of nutrition agenda-setting in the context of the double burden of malnutrition in Tamil Nadu, India through in-depth interviews with state- and national-level nutrition stakeholders (n=28). We used a grounded theory method of analysis to construct stakeholder frames of undernutrition and nutrition-related NCDs and show how different frames held by stakeholders reflect intention and action regarding nutrition policy and programming. We mapped emergent themes to a determinants of political priority framework to identify the conditions and characteristics that support and inhibit the inclusion of nutrition-related NCDs in the nutrition policy agenda. Contrary to the framing of undernutrition, framing of nutrition-related NCDs lacked consistency with respect to health conditions, risk factors, target populations, roles for stakeholders, and program and policy effect on malnutrition. Comparison of the two frames suggests three challenges to bringing nutrition-related NCDs to the state nutrition policy agenda: prioritization of the problem, coherence by the policy community, and convergence of efforts to address them. Implementation of the recently released National Nutrition Strategy presents an opportunity to integrate nutritionrelated NCDs into the state nutrition agenda, but leadership and responsibility among policy actors for addressing them is weak. The ways that nutritionrelated NCDs are understood by stakeholders and portrayed to others highlight the lack of coherence within the policy community and the negative social constructions of those who suffer from them. Efforts to address the double burden of malnutrition at the subnational level must first overcome these barriers

Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study

BACKGROUND: Hypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events. OBJECTIVE: In this study, we aim to develop a deep-learning-based natural language processing (NLP) system to automatically detect hypoglycemic events from EHR notes. Our model is called the High-Performing System for Automatically Detecting Hypoglycemic Events (HYPE). METHODS: Domain experts reviewed 500 EHR notes of diabetes patients to determine whether each sentence contained a hypoglycemic event or not. We used this annotated corpus to train and evaluate HYPE, the high-performance NLP system for hypoglycemia detection. We built and evaluated both a classical machine learning model (ie, support vector machines [SVMs]) and state-of-the-art neural network models. RESULTS: We found that neural network models outperformed the SVM model. The convolutional neural network (CNN) model yielded the highest performance in a 10-fold cross-validation setting: mean precision=0.96 (SD 0.03), mean recall=0.86 (SD 0.03), and mean F1=0.91 (SD 0.03). CONCLUSIONS: Despite the challenges posed by small and highly imbalanced data, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE can be used for EHR-based hypoglycemia surveillance and population studies in diabetes patients

The epigraphy and palaeography of Ceylon down to the 10th century A.D.

The field of study covered by this thesis is the Epigraphy and Palaeography of Ceylon. The following point are discussed: I. The development of the Brahmi Lipi in India and in final disappearance II. The distribution of the lithic records in Ceylon at different periods III. The evolution of the Scrihalese alphabet from the Brahmi Lipi IV. An index of the sidet of known inscriptions is provided with detailed description ou their location and type V. An alphabetical list of inscriptions (published and unpublished) is also provided, giving references to an published inscriptions VI. The development of epigraphical studies in Ceylon since the deciphelment of the Brahmi Lipi upto 1948 VII. (a) Eight maps are provided cidicaling the siles of the inscriptions of different periods (b) one palaeographical chart are provided with complete bramcribed tables of Roman scripti. Conclusion - It is evident that the inscriptions of Asoka, from the subsequent inscriptions of Karle ami Nasih influence the evolution of the Scrihalex alphabet. It is shown that the Brahmi Lipi conlicei in use side by side with the primitive Scrihalex script for about - this cenbiries than it did on Western India considerably lalei than has hilherb been belived, Attention is drawn to the importance of the palaeographical evidence for The study of language and the technology of writing

Evaluating diverse electronic consultation programs with a common framework.

BackgroundElectronic consultation is an emerging mode of specialty care delivery that allows primary care providers and their patients to obtain specialist expertise without an in-person visit. While studies of individual programs have demonstrated benefits related to timely access to specialty care, electronic consultation programs have not achieved widespread use in the United States. The lack of common evaluation metrics across health systems and concerns related to the generalizability of existing evaluation efforts may be hampering further growth. We sought to identify gaps in knowledge related to the implementation of electronic consultation programs and develop a set of shared evaluation measures to promote further diffusion.MethodsUsing a case study approach, we apply the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) and the Quadruple Aim frameworks of evaluation to examine electronic consultation implementation across diverse delivery systems. Data are from 4 early adopter healthcare delivery systems (San Francisco Health Network, Mayo Clinic, Veterans Administration, Champlain Local Health Integration Network) that represent varied organizational structures, care for different patient populations, and have well-established multi-specialty electronic consultation programs. Data sources include published and unpublished quantitative data from each electronic consultation database and qualitative data from systems' end-users.ResultsOrganizational drivers of electronic consultation implementation were similar across the systems (challenges with timely and/or efficient access to specialty care), though unique system-level facilitators and barriers influenced reach, adoption and design. Effectiveness of implementation was consistent, with improved patient access to timely, perceived high-quality specialty expertise with few negative consequences, garnering high satisfaction among end-users. Data about patient-specific clinical outcomes are lacking, as are policies that provide guidance on the legal implications of electronic consultation and ideal remuneration strategies.ConclusionA core set of effectiveness and implementation metrics rooted in the Quadruple Aim may promote data-driven improvements and further diffusion of successful electronic consultation programs

Detecting Hypoglycemia Incidents Reported in Patients\u27 Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance

BACKGROUND: Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although secure messaging was designed for exchanging nonurgent messages, patients sometimes report hypoglycemia events through secure messaging. Detecting these patient-reported adverse events may help alert clinical teams and enable early corrective actions to improve patient safety. OBJECTIVE: We aimed to develop a natural language processing system, called HypoDetect (Hypoglycemia Detector), to automatically identify hypoglycemia incidents reported in patients\u27 secure messages. METHODS: An expert in public health annotated 3000 secure message threads between patients with diabetes and US Department of Veterans Affairs clinical teams as containing patient-reported hypoglycemia incidents or not. A physician independently annotated 100 threads randomly selected from this dataset to determine interannotator agreement. We used this dataset to develop and evaluate HypoDetect. HypoDetect incorporates 3 machine learning algorithms widely used for text classification: linear support vector machines, random forest, and logistic regression. We explored different learning features, including new knowledge-driven features. Because only 114 (3.80%) messages were annotated as positive, we investigated cost-sensitive learning and oversampling methods to mitigate the challenge of imbalanced data. RESULTS: The interannotator agreement was Cohen kappa=.976. Using cross-validation, logistic regression with cost-sensitive learning achieved the best performance (area under the receiver operating characteristic curve=0.954, sensitivity=0.693, specificity 0.974, F1 score=0.590). Cost-sensitive learning and the ensembled synthetic minority oversampling technique improved the sensitivity of the baseline systems substantially (by 0.123 to 0.728 absolute gains). Our results show that a variety of features contributed to the best performance of HypoDetect. CONCLUSIONS: Despite the challenge of data imbalance, HypoDetect achieved promising results for the task of detecting hypoglycemia incidents from secure messages. The system has a great potential to facilitate early detection and treatment of hypoglycemia

Secure Messaging, Diabetes Self-management, and the Importance of Patient Autonomy: a Mixed Methods Study

BACKGROUND: Diabetes is a complex, chronic disease that requires patients\u27 effective self-management between clinical visits; this in turn relies on patient self-efficacy. The support of patient autonomy from healthcare providers is associated with better self-management and greater diabetes self-efficacy. Effective provider-patient secure messaging (SM) through patient portals may improve disease self-management and self-efficacy. SM that supports patients\u27 sense of autonomy may mediate this effect by providing patients ready access to their health information and better communication with their clinical teams. OBJECTIVE: We examined the association between healthcare team-initiated SM and diabetes self-management and self-efficacy, and whether this association was mediated by patients\u27 perceptions of autonomy support from their healthcare teams. DESIGN: We surveyed and analyzed content of messages sent to a sample of patients living with diabetes who use the SM feature on the VA\u27s My HealtheVet patient portal. PARTICIPANTS: Four hundred forty-six veterans with type 2 diabetes who were sustained users of SM. MAIN MEASURES: Proactive (healthcare team-initiated) SM (0 or \u3e /= 1 messages); perceived autonomy support; diabetes self-management; diabetes self-efficacy. KEY RESULTS: Patients who received at least one proactive SM from their clinical team were significantly more likely to engage in better diabetes self-management and report a higher sense of diabetes self-efficacy. This relationship was mediated by the patient\u27s perception of autonomy support. The majority of proactive SM discussed scheduling, referrals, or other administrative content. Patients\u27 responses to team-initiated communication promoted patient engagement in diabetes self-management behaviors. CONCLUSIONS: Perceived autonomy support is important for diabetes self-management and self-efficacy. Proactive communication from clinical teams to patients can help to foster a patient\u27s sense of autonomy and encourage better diabetes self-management and self-efficacy

A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

BACKGROUND: Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes. OBJECTIVE: The aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people. METHODS: NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions. RESULTS: Physician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid\u27s user interface and a number of definitions, and added 4502 more definitions in CoDeMed. CONCLUSIONS: Physician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients

Electronic consultations (E-consults) and their outcomes: a systematic review

OBJECTIVE: Electronic consultations (e-consults) are clinician-to-clinician communications that may obviate face-to-face specialist visits. E-consult programs have spread within the US and internationally despite limited data on outcomes. We conducted a systematic review of the recent peer-reviewed literature on the effect of e-consults on access, cost, quality, and patient and clinician experience and identified the gaps in existing research on these outcomes. MATERIALS AND METHODS: We searched 4 databases for empirical studies published between 1/1/2015 and 2/28/2019 that reported on one or more outcomes of interest. Two investigators reviewed titles and abstracts. One investigator abstracted information from each relevant article, and another confirmed the abstraction. We applied the GRADE criteria for the strength of evidence for each outcome. RESULTS: We found only modest empirical evidence for effectiveness of e-consults on important outcomes. Most studies are observational and within a single health care system, and comprehensive assessments are lacking. For those outcomes that have been reported, findings are generally positive, with mixed results for clinician experience. These findings reassure but also raise concern for publication bias. CONCLUSION: Despite stakeholder enthusiasm and encouraging results in the literature to date, more rigorous study designs applied across all outcomes are needed. Policy makers need to know what benefits may be expected in what contexts, so they can define appropriate measures of success and determine how to achieve them. Informatics Association 2019. This work is written by US Government employees and is in the public domain in the US

Enhancing Mental and Physical Health of Women through Engagement and Retention (EMPOWER): a protocol for a program of research

Abstract Background The Enhancing Mental and Physical health of Women through Engagement and Retention or EMPOWER program represents a partnership with the US Department of Veterans Health Administration (VA) Health Service Research and Development investigators and the VA Office of Women’s Health, National Center for Disease Prevention and Health Promotion, Primary Care-Mental Health Integration Program Office, Women’s Mental Health Services, and the Office of Patient Centered Care and Cultural Transformation. EMPOWER includes three projects designed to improve women Veterans’ engagement and retention in evidence-based care for high-priority health conditions, i.e., prediabetes, cardiovascular, and mental health. Methods/Design The three proposed projects will be conducted in VA primary care clinics that serve women Veterans including general primary care and women’s health clinics. The first project is a 1-year quality improvement project targeting diabetes prevention. Two multi-site research implementation studies will focus on cardiovascular risk prevention and collaborative care to address women Veterans’ mental health treatment needs respectively. All projects will use the evidence-based Replicating Effective Programs (REP) implementation strategy, enhanced with multi-stakeholder engagement and complexity theory. Mixed methods implementation evaluations will focus on investigating primary implementation outcomes of adoption, acceptability, feasibility, and reach. Program-wide organizational-, provider-, and patient-level measures and tools will be utilized to enhance synergy, productivity, and impact. Both implementation research studies will use a non-randomized stepped wedge design. Discussion EMPOWER represents a coherent program of women’s health implementation research and quality improvement that utilizes cross-project implementation strategies and evaluation methodology. The EMPOWER Quality Enhancement Research Initiative (QUERI) will constitute a major milestone for realizing women Veterans’ engagement and empowerment in the VA system. EMPOWER QUERI will be conducted in close partnership with key VA operations partners, such as the VA Office of Women’s Health, to disseminate and spread the programs nationally. Trial registration The two implementation research studies described in this protocol have been registered as required: Facilitating Cardiovascular Risk Screening and Risk Reduction in Women Veterans: Trial registration NCT02991534 , registered 9 December 2016. Implementation of Tailored Collaborative Care for Women Veterans: Trial registration NCT02950961 , registered 21 October 2016