Impact of document consolidation on healthcare providers' perceived workload and information reconciliation tasks: a mixed methods study.

Background: Information reconciliation is a common yet complex and often time-consuming task performed by healthcare providers. While electronic health record systems can receive "outside information" about a patient in electronic documents, rarely does the computer automate reconciling information about a patient across all documents. Materials and Methods: Using a mixed methods design, we evaluated an information system designed to reconcile information across multiple electronic documents containing health records for a patient received from a health information exchange (HIE) network. Nine healthcare providers participated in scenario-based sessions in which they manually consolidated information across multiple documents. Accuracy of consolidation was measured along with the time spent completing 3 different reconciliation scenarios with and without support from the information system. Participants also attended an interview about their experience. Perceived workload was evaluated quantitatively using the NASA-TLX tool. Qualitative analysis focused on providers' impression of the system and the challenges faced when reconciling information in practice. Results: While 5 providers made mistakes when trying to manually reconcile information across multiple documents, no participants made a mistake when the system supported their work. Overall perceived workload decreased significantly for scenarios supported by the system (37.2% in referrals, 18.4% in medications, and 31.5% in problems scenarios, P < 0.001). Information reconciliation time was reduced significantly when the system supported provider tasks (58.8% in referrals, 38.1% in medications, and 65.1% in problem scenarios). Conclusion: Automating retrieval and reconciliation of information across multiple electronic documents shows promise for reducing healthcare providers' task complexity and workload.

Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer Forum.

BACKGROUND: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. OBJECTIVE: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. METHODS: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. RESULTS: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics-based on the Akaike information criterion values ranging from -642.75 to -412.32-were statistically significant. CONCLUSIONS: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life.

Reconciling disparate information in continuity of care documents: Piloting a system to consolidate structured clinical documents.

BACKGROUND: Due to the nature of information generation in health care, clinical documents contain duplicate and sometimes conflicting information. Recent implementation of Health Information Exchange (HIE) mechanisms in which clinical summary documents are exchanged among disparate health care organizations can proliferate duplicate and conflicting information. MATERIALS AND METHODS: To reduce information overload, a system to automatically consolidate information across multiple clinical summary documents was developed for an HIE network. The system receives any number of Continuity of Care Documents (CCDs) and outputs a single, consolidated record. To test the system, a randomly sampled corpus of 522 CCDs representing 50 unique patients was extracted from a large HIE network. The automated methods were compared to manual consolidation of information for three key sections of the CCD: problems, allergies, and medications. RESULTS: Manual consolidation of 11,631 entries was completed in approximately 150h. The same data were automatically consolidated in 3.3min. The system successfully consolidated 99.1% of problems, 87.0% of allergies, and 91.7% of medications. Almost all of the inaccuracies were caused by issues involving the use of standardized terminologies within the documents to represent individual information entries. CONCLUSION: This study represents a novel, tested tool for de-duplication and consolidation of CDA documents, which is a major step toward improving information access and the interoperability among information systems. While more work is necessary, automated systems like the one evaluated in this study will be necessary to meet the informatics needs of providers and health systems in the future.

Consolidating CCDs from multiple data sources: a modular approach.

BACKGROUND: Healthcare providers sometimes receive multiple continuity of care documents (CCDs) for a single patient encompassing the patient's various encounters and medical history recorded in different information systems. It is cumbersome for providers to explore different pages of CCDs to find specific data which can be duplicated or even conflicted. This study describes initial steps toward a modular system that integrates and de-duplicates multiple CCDs into one consolidated document for viewing or processing patient-level data. MATERIALS AND METHODS: The authors developed a prototype system to consolidate and de-duplicate CCDs. The system is engineered to be scalable, extensible, and open source. Using a corpus of 150 de-identified CCDs synthetically generated from a single data source with a common vocabulary to represent 50 unique patients, the authors tested the system's performance and output. Performance was measured based on document throughput and reduction in file size and volume of data. The authors further compared the output of the system with manual consolidation and de-duplication. Testing across multiple vendor systems or implementations was not performed. RESULTS: All of the input CCDs was successfully consolidated, and no data were lost. De-duplication significantly reduced the number of entries in different sections (49% in Problems, 60.6% in Medications, and 79% in Allergies) and reduced the size of the documents (57.5%) as well as the number of lines in each document (58%). The system executed at a rate of approximately 0.009-0.03 s per rule depending on the complexity of the rule. DISCUSSION AND CONCLUSION: Given increasing adoption and use of health information exchange (HIE) to share data and information across the care continuum, duplication of information is inevitable. A novel system designed to support automated consolidation and de-duplication of information across clinical documents as they are exchanged shows promise. Future work is needed to expand the capabilities of the system and further test it using heterogeneous vocabularies across multiple HIE scenarios.

A Service Oriented Architecture Approach to Achieve Interoperability between Immunization Information Systems in Iran.

Clinical decision support (CDS) systems can support vaccine forecasting and immunization reminders; however, immunization decision-making requires data from fragmented, independent systems. Interoperability and accurate data exchange between immunization information systems (IIS) is an essential factor to utilize Immunization CDS systems. Service oriented architecture (SOA) and Health Level 7 (HL7) are dominant standards for web-based exchange of clinical information. We implemented a system based on SOA and HL7 v3 to support immunization CDS in Iran. We evaluated system performance by exchanging 1500 immunization records for roughly 400 infants between two IISs. System turnaround time is less than a minute for synchronous operation calls and the retrieved immunization history of infants were always identical in different systems. CDS generated reports were accordant to immunization guidelines and the calculations for next visit times were accurate. Interoperability is rare or nonexistent between IIS. Since inter-state data exchange is rare in United States, this approach could be a good prototype to achieve interoperability of immunization information.

Frequent loss of heterozygosity at 8p22 chromosomal region in diffuse type of gastric cancer.

AIM: To study the loss of heterozygosity (LOH) at 8p21-23 locus in diffuse gastric cancer. METHODS: To evaluate the involvement of this region in gastric cancer, we used eight microsatellite markers covering two Mb of mentioned region, to perform a high-resolution analysis of allele loss in 42 cases of late diffuse gastric adenocarcinoma. RESULTS: Six of these STS makers: D8S1149, D8S1645, D8S1643, D8S1508, D8S1591, and D8S1145 showed 36%, 28%, 37%, 41%, 44% and 53% LOH, respectively. CONCLUSION: A critical region of loss, close to the NAT2 locus and relatively far from FEZ1 gene currently postulated as tumor suppressor gene in this region.

Molecular and phylogenetic analysis of human influenza virus among Iranian patients in Shiraz, Iran.

Influenza is a viral respiratory pathogen responsible for frequent seasonal epidemics. There are currently three major human influenza viruses in global circulation namely A/H1N1, A/H3N2, and B. The objective of this study was to determine the human influenza virus genotypes in Shiraz, the capital of the Fars province of Iran. Three hundred patients suspected with human influenza virus infection were enrolled in this survey (2004-2005). The throat samples were cultured and titrated by hemagglutination (HA) assay. Typing and subtyping were performed by an in-house developed multiplex RT-PCR. Moreover, the phylogenetic analysis was carried out for HA gene. A total of 24 samples were found to be positive for human influenza virus infection, 17 H1N1 and 7 H3N2. These results were in agreement with the HI assay. The phylogenetic analysis results revealed that the Iranian H1N1 isolated were close to the A/New Caledonia/20/99 vaccine strain genetically and the Iranian H3N2 isolates were also related closely to the Fujian/411/021 and California/7/2004 vaccine strains. However, a slight genetic drift was found in these isolates. In conclusion, it was demonstrated that both influenza A subtypes A/H1N1 and A/H3N2 were dominant among Iranian patients in Shiraz during the 2004/5 winter season.

Different photoresponses of Staphylococcus aureus and Pseudomonas aeruginosa to 514, 532, and 633 nm low level lasers in vitro.

Low Level Light irradiation (LLLI) may proliferate cell growth at certain conditions during a set of photochemical reactions called biostimulation. However, phototoxic inhibitory reactions after irradiating natural or artificially inoculated cells are possible. The purpose of this study was to determine these effects on Pseudomonas aeruginosa and Staphylococcus aureus. Ar ion laser at 514 nm was used to determine the effect of various energy densities of green light on these bacteria. The most effective energy densities of Ar ion laser were chosen to irradiate both bacteria with He-Ne (633 nm) and SHG Nd:YAG (532 nm) lasers to compare the effect of red and green lights on the growth. Irradiation of Pseudomonas aeruginosa for both He-Ne and SHG Nd:YAG lasers in the presence of toluidine blue O or safranine O as photosensitizers was also studied. All energy densities of Ar ion laser showed a proliferative effect on Pseudomonas aeruginosa and inhibitory effect on Staphylococcus aureus. Similarly, SHG Nd:YAG and He-Ne lasers with chosen energy densities were again proliferating for Pseudomonas aeruginosa and inhibitory for Staphylococcus aureus and SHG Nd:YAG was more effective than He-Ne in both cases. Irradiation of Pseudomonas aeruginosa in the presence of both photosensitizers led to the decrease of the cell population compared to the control.