Increasing use of electronic health records (EHRs) provides new opportunities for public health surveillance. During the 2009 influenza A (H1N1) virus pandemic, we developed a new EHR-based influenza-like illness (ILI) surveillance system designed to be resource sparing, rapidly scalable, and flexible. 4 weeks after the first pandemic case, ILI data from Indian Health Service (IHS) facilities were being analyzed.
Materials and methods
The system defines ILI as a patient visit containing either an influenza-specific International Classification of Disease, V.9 (ICD-9) code or one or more of 24 ILI-related ICD-9 codes plus a documented temperature ≥100°F. EHR-based data are uploaded nightly. To validate results, ILI visits identified by the new system were compared to ILI visits found by medical record review, and the new system's results were compared with those of the traditional US ILI Surveillance Network.
The system monitored ILI activity at an average of 60% of the 269 IHS electronic health databases. EHR-based surveillance detected ILI visits with a sensitivity of 96.4% and a specificity of 97.8% based on chart review (N=2375) of visits at two facilities in September 2009. At the peak of the pandemic (week 41, October 17, 2009), the median time from an ILI visit to data transmission was 6 days, with a mode of 1 day.
EHR-based ILI surveillance was accurate, timely, occurred at the majority of IHS facilities nationwide, and provided useful information for decision makers. EHRs thus offer the opportunity to transform public health surveillance.
Influenza, Human; Population Surveillance; Epidemiologic Methods; Electronic Health Records; Medical Informatics Applications; Data Collection
To model inconsistencies or distortions among three realities: patients' physical reality; clinicians' mental models of patients' conditions, laboratories, etc; representation of that reality in electronic health records (EHR). To serve as a potential tool for quality improvement of EHRs.
Using observations, literature, information technology (IT) logs, vendor and US Food and Drug Administration reports, we constructed scenarios/models of how patients' realities, clinicians' mental models, and EHRs can misalign to produce distortions in comprehension and treatment. We then categorized them according to an emergent typology derived from the cases themselves and refined the categories based on insights gained from the literature of interactive sociotechnical systems analysis, decision support science, and human computer interaction. Typical of grounded theory methods, the categories underwent repeated modifications.
We constructed 45 scenarios of misalignment between patients' physical realities, clinicians' mental models, and EHRs. We then identified five general types of misrepresentation in these cases: IT data too narrowly focused; IT data too broadly focused; EHRs miss critical reality; data multiplicities–perhaps contradictory or confusing; distortions from data reflected back and forth across users, sensors, and others. The 45 scenarios are presented, organized by the five types.
With humans, there is a physical reality and actors' mental models of that reality. In healthcare, there is another player: the EHR/healthcare IT, which implicitly and explicitly reflects many mental models, facets of reality, and measures thereof that vary in reliability and consistency. EHRs are both microcosms and shapers of medical care. Our typology and scenarios are intended to be useful to healthcare IT designers and implementers in improving EHR systems and reducing the unintended negative consequences of their use.
Modeling Interactions; Continuous quality improvement; Typology Development; Quality improvement
We aim to identify duplicate pairs of Medline citations, particularly when the documents are not identical but contain similar information.
Materials and methods
Duplicate pairs of citations are identified by comparing word n-grams in pairs of documents. N-grams are modified using two approaches which take account of the fact that the document may have been altered. These are: (1) deletion, an item in the n-gram is removed; and (2) substitution, an item in the n-gram is substituted with a similar term obtained from the Unified Medical Language System Metathesaurus. N-grams are also weighted using a score derived from a language model. Evaluation is carried out using a set of 520 Medline citation pairs, including a set of 260 manually verified duplicate pairs obtained from the Deja Vu database.
The approach accurately detects duplicate Medline document pairs with an F1 measure score of 0.99. Allowing for word deletions and substitution improves performance. The best results are obtained by combining scores for n-grams of length 1–5 words.
Results show that the detection of duplicate Medline citations can be improved by modifying n-grams and that high performance can also be obtained using only unigrams (F1=0.959), particularly when allowing for substitutions of alternative phrases.
Natural Language Processing; PubMed
Despite the potential for electronic health records to help providers coordinate care, the current marketplace has failed to provide adequate solutions. Using a simple framework, we describe a vision of information technology capabilities that could substantially improve four care coordination activities: identifying collaborators, contacting collaborators, collaborating, and monitoring. Collaborators can include any individual clinician, caregiver, or provider organization involved in care for a given patient. This vision can be used to guide the development of care coordination tools and help policymakers track and promote their adoption.
care coordination; electronic health records; health information technology policy; Informatics research; IT Vision
The aim of this study was to assess the accuracy of clinician-entered data in imaging clinical decision support (CDS). We used CDS-guided CT angiography (CTA) for pulmonary embolus (PE) in the emergency department as a case example because it required clinician entry of d-dimer results which could be unambiguously compared with actual laboratory values. Of 1296 patients with CTA orders for suspected PE during 2011, 1175 (90.7%) had accurate d-dimer values entered. In 55 orders (4.2%), incorrectly entered data shielded clinicians from intrusive computer alerts, resulting in potential CTA overuse. Remaining data entry errors did not affect user workflow. We found no missed PEs in our cohort. The majority of data entered by clinicians into imaging CDS are accurate. A small proportion may be intentionally erroneous to avoid intrusive computer alerts. Quality improvement methods, including academic detailing and improved integration between electronic medical record and CDS to minimize redundant data entry, may be necessary to optimize adoption of evidence presented through CDS.
Decision Support Systems, Clinical; Computerized Physician Order Entry System; Radiology; Electronic Health Records; Evidence-Based Practice
The Omaha System (OS) is one of the oldest of the American Nurses Association recognized standardized terminologies describing and measuring the impact of healthcare services. This systematic review presents the state of science on the use of the OS in practice, research, and education.
(1) To identify, describe and evaluate the publications on the OS between 2004 and 2011, (2) to identify major trends in the use of the OS in research, practice, and education, and (3) to suggest areas for future research.
Systematic search in the largest online healthcare databases (PUBMED, CINAHL, Scopus, PsycINFO, Ovid) from 2004 to 2011. Methodological quality of the reviewed research studies was evaluated.
56 publications on the OS were identified and analyzed. The methodological quality of the reviewed research studies was relatively high. Over time, publications’ focus shifted from describing clients’ problems toward outcomes research. There was an increasing application of advanced statistical methods and a significant portion of authors focused on classification and interoperability research. There was an increasing body of international literature on the OS. Little research focused on the theoretical aspects of the OS, the effective use of the OS in education, or cultural adaptations of the OS outside the USA.
The OS has a high potential to provide meaningful and high quality information about complex healthcare services. Further research on the OS should focus on its applicability in healthcare education, theoretical underpinnings and international validity. Researchers analyzing the OS data should address how they attempted to mitigate the effects of missing data in analyzing their results and clearly present the limitations of their studies.
Nursing Informatics; Terminology as Topic; Data Mining; Omaha System; Nursing Classification
Epilepsy encompasses an extensive array of clinical and research subdomains, many of which emphasize multi-modal physiological measurements such as electroencephalography and neuroimaging. The integration of structured, unstructured, and signal data into a coherent structure for patient care as well as clinical research requires an effective informatics infrastructure that is underpinned by a formal domain ontology.
We have developed an epilepsy and seizure ontology (EpSO) using a four-dimensional epilepsy classification system that integrates the latest International League Against Epilepsy terminology recommendations and National Institute of Neurological Disorders and Stroke (NINDS) common data elements. It imports concepts from existing ontologies, including the Neural ElectroMagnetic Ontologies, and uses formal concept analysis to create a taxonomy of epilepsy syndromes based on their seizure semiology and anatomical location.
EpSO is used in a suite of informatics tools for (a) patient data entry, (b) epilepsy focused clinical free text processing, and (c) patient cohort identification as part of the multi-center NINDS-funded study on sudden unexpected death in epilepsy. EpSO is available for download at http://prism.case.edu/prism/index.php/EpilepsyOntology.
An epilepsy ontology consortium is being created for community-driven extension, review, and adoption of EpSO. We are in the process of submitting EpSO to the BioPortal repository.
EpSO plays a critical role in informatics tools for epilepsy patient care and multi-center clinical research.
Epilepsy and Seizure Ontology; Patient Data Capture; Clinical Free Text Processing; Clinical Data Integration
Many mobile phone resources have been developed to increase access to health education in the developing world, yet few studies have compared these resources or quantified their performance in a resource-limited setting. This study aims to compare the performance of resident physicians in answering clinical scenarios using PubMed abstracts accessed via the PubMed for Handhelds (PubMed4Hh) website versus medical/drug reference applications (Medical Apps) accessed via software on the mobile phone.
A two-arm comparative study with crossover design was conducted. Subjects, who were resident physicians at the University of Botswana, completed eight scenarios, each with multi-part questions. The primary outcome was a grade for each question. The primary independent variable was the intervention arm and other independent variables included residency and question.
Within each question type there were significant differences in ‘percentage correct’ between Medical Apps and PubMed4Hh for three of the six types of questions: drug-related, diagnosis/definitions, and treatment/management. Within each of these question types, Medical Apps had a higher percentage of fully correct responses than PubMed4Hh (63% vs 13%, 33% vs 12%, and 41% vs 13%, respectively). PubMed4Hh performed better for epidemiologic questions.
While mobile access to primary literature remains important and serves an information niche, mobile applications with condensed content may be more appropriate for point-of-care information needs. Further research is required to examine the specific information needs of clinicians in resource-limited settings and to evaluate the appropriateness of current resources in bridging location- and context-specific information gaps.
mobile phones; mobile health; decision making; mHealthEd
Recently, an important public debate emerged about the digital afterlife of any personal data stored in the cloud. Such debate brings also to attention the importance of transparent management of electronic health record (EHR) data of deceased patients. In this perspective paper, we look at legal and regulatory policies for EHR data post mortem. We analyze observational research situations using EHR data that do not require institutional review board approval. We propose creation of a deceased subject integrated data repository (dsIDR) as an effective tool for piloting certain types of research projects. We highlight several dsIDR challenges in proving death status, informed consent, obtaining data from payers and healthcare providers and the involvement of next of kin.
electronic health record ; research policy; post mortem; privacy; integrated data repository; institutional review board
To develop a conceptual framework for the design of an in-home monitoring system (IMS) based on the requirements of older adults with vision impairment (VI), informal caregivers and eye-care rehabilitation professionals.
Materials and Methods
Concept mapping, a mixed-methods statistical research tool, was used in the construction of the framework. Overall, 40 participants brainstormed or sorted and rated 83 statements concerning an IMS for older adults with VI. Multidimensional scaling and hierarchical cluster analysis were employed to construct the framework. A questionnaire yielded further insights into the views of a wider sample of older adults with VI (n=78) and caregivers (n=25) regarding IMS.
Concept mapping revealed a nine-cluster model of IMS-related aspects including affordability, awareness of system capabilities, simplicity of installation, operation and maintenance, system integrity and reliability, fall detection and safe movement, user customization, user preferences regarding information delivery, and safety alerts for patients and caregivers. From the questionnaire, independence, safety and fall detection were the most commonly reported reasons for older adults and caregivers to accept an IMS. Concerns included cost, privacy, security of the information obtained through monitoring, system accuracy, and ease of use.
Older adults with VI, caregivers and professionals are receptive to in-home monitoring, mainly for fall detection and safety monitoring, but have concerns that must be addressed when developing an IMS.
Our study provides a novel conceptual framework for the design of an IMS that will be maximally acceptable and beneficial to our ageing and vision-impaired population.
In-home monitoring; in-home monitoring system (IMS); assistive technology; elderly; vision impairment
A comprehensive and machine-understandable cancer drug–side effect (drug–SE) relationship knowledge base is important for in silico cancer drug target discovery, drug repurposing, and toxicity predication, and for personalized risk–benefit decisions by cancer patients. While US Food and Drug Administration (FDA) drug labels capture well-known cancer drug SE information, much cancer drug SE knowledge remains buried the published biomedical literature. We present a relationship extraction approach to extract cancer drug–SE pairs from the literature.
Data and methods
We used 21 354 075 MEDLINE records as the text corpus. We extracted drug–SE co-occurrence pairs using a cancer drug lexicon and a clean SE lexicon that we created. We then developed two filtering approaches to remove drug–disease treatment pairs and subsequently a ranking scheme to further prioritize filtered pairs. Finally, we analyzed relationships among SEs, gene targets, and indications.
We extracted 56 602 cancer drug–SE pairs. The filtering algorithms improved the precision of extracted pairs from 0.252 at baseline to 0.426, representing a 69% improvement in precision with no decrease in recall. The ranking algorithm further prioritized filtered pairs and achieved a precision of 0.778 for top-ranked pairs. We showed that cancer drugs that share SEs tend to have overlapping gene targets and overlapping indications.
The relationship extraction approach is effective in extracting many cancer drug–SE pairs from the literature. This unique knowledge base, when combined with existing cancer drug SE knowledge, can facilitate drug target discovery, drug repurposing, and toxicity prediction.
Information Extraction; Text Mining; Cancer Drug Toxicity; Natural Language Processing; Drug Target Discovery; Drug Repurposing
To examine the use of online social networking for cardiovascular care using Facebook. All posts and comments in a Facebook group between June 2011 and May 2012 were reviewed, and a survey was conducted. A total of 298 members participated. Of the 277 wall posts, 26.7% were question posts requesting rapid replies, and 50.5% were interesting cases shared with other members. The median response time for the question posts was 16 min (IQR 8–47), which tended to decrease as more members joined the group. Many members (37.4%) accessed the group more than once a day, and more than half (64%) monitored the group posts in real time with automatic notifications of new posts. Most members expressed confidence in the content posted. Facebook enables online social networking between physicians in near-real time and appears to be a useful tool for physicians to share clinical experience and request assistance in decision-making.
social network; cardiac care
To assess the state of readiness for the adoption of paperless labeling among a nationally representative sample of pharmacies, including chain pharmacies, independent retail pharmacies, hospitals, and other rural or urban dispensing sites.
Both quantitative and qualitative analyses were used to analyze responses to a cross-sectional survey disseminated to American Pharmacists Association pharmacists nationwide. The survey assessed factors related to pharmacists’ attitudinal readiness (ie, perceptions of impact) and pharmacies’ structural readiness (eg, availability of electronic resources, internet access) for the paperless labeling initiative.
We received a total of 436 survey responses (6% response rate) from pharmacists representing 44 US states and territories. Across the spectrum of settings we studied, pharmacists had work access to computers, printers, fax machines and access to the internet or intranet. Approximately 79% of respondents believed that the initiative would improve the adequacy of drug information available in their work site and 95% believed it would either not change (33%) or would improve (62%) communication to patients. Overall, respondents’ comments supported advancing the initiative; however, some comments revealed reservations regarding corporate or pharmacy buy-in, success of implementation, and ease of adoption.
This is the first nationwide study to report about pharmacists’ perspectives on paperless labeling. In general, pharmacists believe they are ready and that their pharmacies are well equipped for the transition to paperless labeling. Further exploration of perspectives from product label manufacturers and corporate pharmacy offices is needed to understand fully what will be necessary to complete this transition.
Drug Information Services; Feasibility Studies; Pharmacies; Pharmacists; Product labeling/methods
High-performance computing centers (HPC) traditionally have far less restrictive privacy management policies than those encountered in healthcare. We show how an HPC can be re-engineered to accommodate clinical data while retaining its utility in computationally intensive tasks such as data mining, machine learning, and statistics. We also discuss deploying protected virtual machines. A critical planning step was to engage the university's information security operations and the information security and privacy office. Access to the environment requires a double authentication mechanism. The first level of authentication requires access to the university's virtual private network and the second requires that the users be listed in the HPC network information service directory. The physical hardware resides in a data center with controlled room access. All employees of the HPC and its users take the university's local Health Insurance Portability and Accountability Act training series. In the first 3 years, researcher count has increased from 6 to 58.
High-performance Computing; Translational Medical Research; Clinical Research Informatics; HIPAA
To develop benchmark measures of health information and communication technology (ICT) use to facilitate cross-country comparisons and learning.
Materials and methods
The effort is led by the Organisation for Economic Co-operation and Development (OECD). Approaches to definition and measurement within four ICT domains were compared across seven OECD countries in order to identify functionalities in each domain. These informed a set of functionality-based benchmark measures, which were refined in collaboration with representatives from more than 20 OECD and non-OECD countries. We report on progress to date and remaining work to enable countries to begin to collect benchmark data.
The four benchmarking domains include provider-centric electronic record, patient-centric electronic record, health information exchange, and tele-health. There was broad agreement on functionalities in the provider-centric electronic record domain (eg, entry of core patient data, decision support), and less agreement in the other three domains in which country representatives worked to select benchmark functionalities.
Many countries are working to implement ICTs to improve healthcare system performance. Although many countries are looking to others as potential models, the lack of consistent terminology and approach has made cross-national comparisons and learning difficult.
As countries develop and implement strategies to increase the use of ICTs to promote health goals, there is a historic opportunity to enable cross-country learning. To facilitate this learning and reduce the chances that individual countries flounder, a common understanding of health ICT adoption and use is needed. The OECD-led benchmarking process is a crucial step towards achieving this.
definitions of health IT; cross-country benchmarking; adoption of health IT; health policy
Given the complexities of the healthcare environment, efforts to develop standardized handoff practices have led to widely varying manifestations of handoff tools. A systematic review of the literature on handoff evaluation studies was performed to investigate the nature, methodological, and theoretical foundations underlying the evaluation of handoff tools and their adequacy and appropriateness in achieving standardization goals.
We searched multiple databases for articles evaluating handoff tools published between 1 February 1983 and 15 June 2012. The selected articles were categorized along the following dimensions: handoff tool characteristics, standardization initiatives, methodological framework, and theoretical perspectives underlying the evaluation.
Thirty-six articles met our inclusion criteria. Handoff evaluations were conducted primarily on electronic tools (64%), with a more recent focus on electronic medical record-integrated tools (36% since 2008). Most evaluations centered on intra-departmental tools (95%). Evaluation studies were quasi-experimental (42%) or observational (50%), with a major focus on handoff-related outcome measures (94%) using predominantly survey-based tools (70%) with user satisfaction metrics (53%). Most of the studies (81%) based their evaluation on aspects of standardization that included continuity of care and patient safety.
The nature, methodological, and theoretical foundations of handoff tool evaluations varied significantly in terms of their quality and rigor, thereby limiting their ability to inform strategic standardization initiatives. Future research should utilize rigorous, multi-method qualitative and quantitative approaches that capture the contextual nuances of handoffs, and evaluate their effect on patient-related outcomes.
Handoff Tools; Care Continuity; Patient Safety; Evaluation Method; Standardization
To better understand the relationship between online health-seeking behaviors and in-world healthcare utilization (HU) by studies of online search and access activities before and after queries that pursue medical professionals and facilities.
Materials and methods
We analyzed data collected from logs of online searches gathered from consenting users of a browser toolbar from Microsoft (N=9740). We employed a complementary survey (N=489) to seek a deeper understanding of information-gathering, reflection, and action on the pursuit of professional healthcare.
We provide insights about HU through the survey, breaking out its findings by different respondent marginalizations as appropriate. Observations made from search logs may be explained by trends observed in our survey responses, even though the user populations differ.
The results provide insights about how users decide if and when to utilize healthcare resources, and how online health information seeking transitions to in-world HU. The findings from both the survey and the logs reveal behavioral patterns and suggest a strong relationship between search behavior and HU. Although the diversity of our survey respondents is limited and we cannot be certain that users visited medical facilities, we demonstrate that it may be possible to infer HU from long-term search behavior by the apparent influence that health concerns and professional advice have on search activity.
Our findings highlight different phases of online activities around queries pursuing professional healthcare facilities and services. We also show that it may be possible to infer HU from logs without tracking people's physical location, based on the effect of HU on pre- and post-HU search behavior. This allows search providers and others to develop more robust models of interests and preferences by modeling utilization rather than simply the intention to utilize that is expressed in search queries.
Healthcare utilization; Healthcare seeking; Search log analysis; User surveys
Multimorbidity, the co-occurrence of two or more chronic medical conditions within a single individual, is increasingly becoming part of daily care of general medical practice. Literature-based discovery may help to investigate the patterns of multimorbidity and to integrate medical knowledge for improving healthcare delivery for individuals with co-occurring chronic conditions.
To explore the usefulness of literature-based discovery in primary care research through the key-case of finding associations between psychiatric and somatic diseases relevant to general practice in a large biomedical literature database (Medline).
By using literature based discovery for matching disease profiles as vectors in a high-dimensional associative concept space, co-occurrences of a broad spectrum of chronic medical conditions were matched for their potential in biomedicine. An experimental setting was chosen in parallel with expert evaluations and expert meetings to assess performance and to generate targets for integrating literature-based discovery in multidisciplinary medical research of psychiatric and somatic disease associations.
Through stepwise reductions a reference set of 21 945 disease combinations was generated, from which a set of 166 combinations between psychiatric and somatic diseases was selected and assessed by text mining and expert evaluation.
Literature-based discovery tools generate specific patterns of associations between psychiatric and somatic diseases: one subset was appraised as promising for further research; the other subset surprised the experts, leading to intricate discussions and further eliciting of frameworks of biomedical knowledge. These frameworks enable us to specify targets for further developing and integrating literature-based discovery in multidisciplinary research of general practice, psychology and psychiatry, and epidemiology.
literature based discovery; text mining; multimorbidity; primary care research; disease profiles; disease combinations
Clinical databases require accurate entity resolution (ER). One approach is to use algorithms that assign questionable cases to manual review. Few studies have compared the performance of common algorithms for such a task. Furthermore, previous work has been limited by a lack of objective methods for setting algorithm parameters. We compared the performance of common ER algorithms: using algorithmic optimization, rather than manual parameter tuning, and on two-threshold classification (match/manual review/non-match) as well as single-threshold (match/non-match).
We manually reviewed 20 000 randomly selected, potential duplicate record-pairs to identify matches (10 000 training set, 10 000 test set). We evaluated the probabilistic expectation maximization, simple deterministic and fuzzy inference engine (FIE) algorithms. We used particle swarm to optimize algorithm parameters for a single and for two thresholds. We ran 10 iterations of optimization using the training set and report averaged performance against the test set.
The overall estimated duplicate rate was 6%. FIE and simple deterministic algorithms allowed a lower manual review set compared to the probabilistic method (FIE 1.9%, simple deterministic 2.5%, probabilistic 3.6%; p<0.001). For a single threshold, the simple deterministic algorithm performed better than the probabilistic method (positive predictive value 0.956 vs 0.887, sensitivity 0.985 vs 0.887, p<0.001). ER with FIE classifies 98.1% of record-pairs correctly (1/10 000 error rate), assigning the remainder to manual review.
Optimized deterministic algorithms outperform the probabilistic method. There is a strong case for considering optimized deterministic methods for ER.
Medical Records Systems, Computerized [L01.700.508.300.695]; Medical Record Linkage [N04.452.859.564.550]
To determine whether the knowledge contained in a rich corpus of local terms mapped to LOINC (Logical Observation Identifiers Names and Codes) could be leveraged to help map local terms from other institutions.
We developed two models to test our hypothesis. The first based on supervised machine learning was created using Apache's OpenNLP Maxent and the second based on information retrieval was created using Apache's Lucene. The models were validated by a random subsampling method that was repeated 20 times and that used 80/20 splits for training and testing, respectively. We also evaluated the performance of these models on all laboratory terms from three test institutions.
For the 20 iterations used for validation of our 80/20 splits Maxent and Lucene ranked the correct LOINC code first for between 70.5% and 71.4% and between 63.7% and 65.0% of local terms, respectively. For all laboratory terms from the three test institutions Maxent ranked the correct LOINC code first for between 73.5% and 84.6% (mean 78.9%) of local terms, whereas Lucene's performance was between 66.5% and 76.6% (mean 71.9%). Using a cut-off score of 0.46 Maxent always ranked the correct LOINC code first for over 57% of local terms.
This study showed that a rich corpus of local terms mapped to LOINC contains collective knowledge that can help map terms from other institutions. Using freely available software tools, we developed a data-driven automated approach that operates on term descriptions from existing mappings in the corpus. Accurate and efficient automated mapping methods can help to accelerate adoption of vocabulary standards and promote widespread health information exchange.
automated mapping; LOINC; local laboratory tests; health information exchange; supervised machine learning; information retrieval
Individual users’ attitudes and opinions help predict successful adoption of health information technology (HIT) into practice; however, little is known about pediatric users’ acceptance of HIT for medical decision-making at the point of care.
Materials and methods
We wished to examine the attitudes and opinions of pediatric users’ toward the Child Health Improvement through Computer Automation (CHICA) system, a computer decision support system linked to an electronic health record in four community pediatric clinics. Surveys were administered in 2011 and 2012 to all users to measure CHICA's acceptability and users’ satisfaction with it. Free text comments were analyzed for themes to understand areas of potential technical refinement.
70 participants completed the survey in 2011 (100% response rate) and 64 of 66 (97% response rate) in 2012. Initially, satisfaction with CHICA was mixed. In general, users felt the system held promise; however various critiques reflected difficulties understanding integrated technical aspects of how CHICA worked, as well as concern with the format and wording on generated forms for families and users. In the subsequent year, users’ ratings reflected improved satisfaction and acceptance. Comments also reflected a deeper understanding of the system's logic, often accompanied by suggestions on potential refinements to make CHICA more useful at the point of care.
Pediatric users appreciate the system's automation and enhancements that allow relevant and meaningful clinical data to be accessible at point of care. Understanding users’ acceptability and satisfaction is critical for ongoing refinement of HIT to ensure successful adoption into practice.
Computer-based decision support; Pediatrics; Clinical guidelines; Primary care