A low-cost ultrasound program leads to increased antenatal clinic visits and attended deliveries at a health care clinic in rural Uganda.

BACKGROUND: In June of 2010, an antenatal ultrasound program to perform basic screening for high-risk pregnancies was introduced at a community health care center in rural Uganda. Whether the addition of ultrasound scanning to antenatal visits at the health center would encourage or discourage potential patients was unknown. Our study sought to evaluate trends in the numbers of antenatal visits and deliveries at the clinic, pre- and post-introduction of antenatal ultrasound to determine what effect the presence of ultrasound at the clinic had on these metrics. METHODS AND FINDINGS: Records at Nawanyago clinic were reviewed to obtain the number of antenatal visits and deliveries for the 42 months preceding the introduction of ultrasound and the 23 months following. The monthly mean deliveries and antenatal visits by category (first visit through fourth return visit) were compared pre- and post- ultrasound using a Kruskal-Wallis one-way ANOVA. Following the introduction of ultrasound, significant increases were seen in the number of mean monthly deliveries and antenatal visits. The mean number of monthly deliveries at the clinic increased by 17.0 (13.3-20.6, 95% CI) from a pre-ultrasound average of 28.4 to a post-ultrasound monthly average of 45.4. The number of deliveries at a comparison clinic remained flat over this same time period. The monthly mean number of antenatal visits increased by 97.4 (83.3-111.5, 95% CI) from a baseline monthly average of 133.5 to a post-ultrasound monthly mean of 231.0, with increases seen in all categories of antenatal visits. CONCLUSIONS: The availability of a low-cost antenatal ultrasound program may assist progress towards Millennium Development Goal 5 by encouraging women in a rural environment to come to a health care facility for skilled antenatal care and delivery assistance instead of utilizing more traditional methods.

The eICU research institute - a collaboration between industry, health-care providers, and academia.

As the volume of data that is electronically available promliferates, the health-care industry is identifying better ways to use this data for patient care. Ideally, these data are collected in real time, can support point-of-care clinical decisions, and, by providing instantaneous quality metrics, can create the opportunities to improve clinical practice as the patient is being cared for. The business-world technology supporting these activities is referred to as business intelligence, which offers competitive advantage, increased quality, and operational efficiencies. The health-care industry is plagued by many challenges that have made it a latecomer to business intelligence and data-mining technology, including delayed adoption of electronic medical records, poor integration between information systems, a lack of uniform technical standards, poor interoperability between complex devices, and the mandate to rigorously protect patient privacy. Efforts at developing a health care equivalent of business intelligence (which we will refer to as clinical intelligence) remains in its infancy. Until basic technology infrastructure and mature clinical applications are developed and implemented throughout the health-care system, data aggregation and interpretation cannot effectively progress. The need for this approach in health care is undisputed. As regional and national health information networks emerge, we need to develop cost-effective systems that reduce time and effort spent documenting health-care data while increasing the application of knowledge derived from that data.

Towards a reference model for representing nursing information needs.

Information needs studies in clinical settings often face the problem of integrating and analyzing data collected using different study tools. Generally, a coding taxonomy or a model is developed to capture and code the study data. Significant efforts are required to develop a model that not only captures the study data but is also closer to the clinical domain to draw meaningful real world inferences. Further, a study-specific model limits comparative evaluation of studies across different institutions. In this paper, we propose a reference model for representing nursing information needs. We use an iterative and collaborative approach in representing the concepts in the model. The model consists of 33 information (need) concepts, 29 information sources and 7 categories of nursing tasks which were used to code 228 identified information need instances.

Trusted and relevant medical knowledge: the promise of information retrieval in biomedicine.

As the world of medicine becomes increasingly digitized, the Web has become a de facto resource for physicians to quickly glean pertinent clinical information to carry out diagnostic and therapeutic decisions. At present, physicians face the dual challenge of judging the relevance of the information and trusting its Web source. This paper proposes a trust-relevance framework for conceptualizing computer-accessed medical information resources, a set of criteria for evaluating these information resources, and descriptions of a sample of available online resources. It also presents a usable framework for evaluating information retrieval innovations and explains the different capabilities of representative information retrieval tools and applications. By demystifying the concepts associated with information resources, search engines, and retrieval tools, and presenting a reasonable view of current opportunities as well as future possibilities, the authors hope to provide guidance so physicians can more rapidly adopt innovative computer-assisted search tools for acquiring information that facilitate patient care decision-making.

Documentation and coding of ED patient encounters: an evaluation of the accuracy of an electronic medical record.

OBJECTIVE: The aim of the study was to describe a paper-based, template-driven and an electronic medical record used for capturing emergency care clinical information and to compare the accuracy of these documentation systems for coding patient encounters using the American Medical Association Current Procedural Terminology-2004 (AMA CPT-2004) evaluation and management codes intended for provider reimbursement. METHODS: A retrospective, cross-sectional study of 4-consecutive-day samples of ED patient encounter records from 2 similar community hospitals was done. For clinical documentation, hospital A uses an electronic medical record, whereas hospital B uses a paper-based template-driven record. Using a simple analytic model, expert coders A and B, respectively, coded the records from hospitals A and B for completeness. First, power analysis determined the acceptability of the patient record sample sizes (1 - beta = .90 at 1% significance level), and the frequency of AMA CPT-2004 primary evaluation and management codes 99281 through 99285 was calculated. Second, the completeness discrepancy rates for hospitals A and B were compared to determine the accuracy of both the paper-based, template-driven record and the electronic medical record in documenting and representing the clinical encounter. Third, interrater reliability between expert coders A and B was calculated to assess the level of agreement between each expert coder in determining the completeness discrepancy rates between hospitals A and B. Finally, the frequency of primary evaluation and management codes was analyzed to determine if there was a statistically significant difference between the paper-based, template-driven record and the electronic medical record representation of the clinical information, and if that difference could be attributable to the differing clinical documentation systems used in hospitals A and B. RESULTS: First, descriptive display demonstrated a difference in the frequency of the primary evaluation and management codes 99283 and 99284 within hospital A (expert coder A assessment, 36.1% vs 39.1%; expert coder B assessment, 36.6% vs 38.7%) and hospital B (expert coder A assessment, 47.8% vs 21.9%; expert coder B assessment, 48.6% vs 21.4%) was noted with the median, primary evaluation, and management code for hospital A of 99284 and the median, primary evaluation, and management code for hospital B of 99283. Second, Fisher exact test compared the completeness discrepancy rates between hospitals A and B as assessed by each expert coder and demonstrated no statistically significant difference in the completeness discrepancy rates (accuracy) between the paper-based, template-driven record and the electronic medical record documentation and coding system when assessed by either expert coder A (P = .370) or expert coder B (P = .819). Third, interrater reliability between expert coders A and B was evaluated using Cohen's kappa statistic. When evaluated both individually and jointly with respect to hospitals A and B, expert coders A and B had a good strength of agreement in their assessments of the accuracy of the documentation and coding system for hospital A (kappa = 0.6200) and hospital B (kappa = 0.6906) as well as for both hospitals evaluated together (kappa = 0.6616). Finally, interhospital differences in the frequency of primary evaluation and management codes were evaluated using Pearson chi(2) test with 3 df. The results for expert coder A (chi(2) = 47.4160; P < .001) and expert coder B (chi(2) = 46.5946; P < .001) recognize that there is a statistically significant degree of difference between hospitals A and B in the frequency distribution of primary evaluation and management codes, probably because of the dispersion of codes 99283 and 99284. CONCLUSIONS: A keystroke-driven, electronic medical record that resides on a knowledge platform that incorporates a clinical structured terminology, administrative coding schemata, AMA CPT-2004 codes and uses object-oriented, open-ended, branching chain clinical algorithms that "force" physician documentation of the clinical elements provides an equally accurate capture and representation of ED clinical encounter data as a paper-based, template-driven documentation system both in terms of the presence or absence of both the medically necessary, discrete data elements and the textual documentation-dependent, medical decision-making elements.

ZebraHunter: searching rare medical diagnoses and retrieving relevant citations.

The clinicopathological conferences and case reports that are published in the medical literature contain rare and complex medical cases that are of general interest to the medical community. We present ZebraHunter, an information retrieval resource that allows clinicians to input clinical findings, search for rare diseases (medical zebras), and fetch the associated citations.