As the public interest in consumer-driven electronic health care applications rises, so do concerns about the privacy and security of these applications. Achieving a balance between providing the necessary security while promoting user acceptance is a major obstacle in large-scale deployment of applications such as personal health records (PHRs). Robust and reliable forms of authentication are needed for PHRs, as the record will often contain sensitive and protected health information, including the patient's own annotations. Since the health care industry per se is unlikely to succeed at single-handedly developing and deploying a large scale, national authentication infrastructure, it makes sense to leverage existing hardware, software, and networks. This report proposes a new model for authentication of users to health care information applications, leveraging wireless mobile devices. Cell phones are widely distributed, have high user acceptance, and offer advanced security protocols. The authors propose harnessing this technology for the strong authentication of individuals by creating a registration authority and an authentication service, and examine the problems and promise of such a system.

BACKGROUND: Data stored in personally controlled health records (PCHRs) may hold value for clinicians and public health entities, if patients and their families will share them. We sought to characterize consumer willingness and unwillingness (reticence) to share PCHR data across health topics, and with different stakeholders, to advance understanding of this issue. METHODS: Cross-sectional 2009 Web survey of repeat PCHR users who were patients over 18 years old or parents of patients, to assess willingness to share their PCHR data with an-out-of-hospital provider to support care, and the state/local public health authority to support monitoring; the odds of reticence to share PCHR information about ten exemplary health topics were estimated using a repeated measures approach. RESULTS: Of 261 respondents (56% response rate), more reported they would share all information with the state/local public health authority (63.3%) than with an out-of-hospital provider (54.1%) (OR 1.5, 95% CI 1.1, 1.9; p = .005); few would not share any information with these parties (respectively, 7.9% and 5.2%). For public health sharing, reticence was higher for most topics compared to contagious illness (ORs 4.9 to 1.4, all p-values .05), and reflected concern about anonymity (47.2%), government insensitivity (41.5%), discrimination (24%). For provider sharing, reticence was higher for all topics compared to contagious illness (ORs 6.3 to 1.5, all p-values .05), and reflected concern for relevance (52%), disclosure to insurance (47.6%) and/or family (20.5%). CONCLUSIONS: Pediatric patients and their families are often willing to share electronic health information to support health improvement, but remain cautious. Robust trust models for PCHR sharing are needed.

Personally controlled health records (PCHRs) enable patients to store, manage, and share their own health data, and promise unprecedented consumer access to medical information. To deploy a PCHR in the pediatric population requires crafting of access and security policies, tailored to a record that is not only under patient control, but one that may also be accessed by parents, guardians, and third-party entities. Such hybrid control of health information requires careful consideration of both the PCHR vendor's access policies, as well as institutional policies regulating data feeds to the PCHR, to ensure that the privacy and confidentiality of each user is preserved. Such policies must ensure compliance with legal mandates to prevent unintended disclosures and must preserve the complex interactions of the patient-provider relationship. Informed by our own operational involvement in the implementation of the Indivo PCHR, we provide a framework for understanding and addressing the challenges posed by child, adolescent, and family access to PCHRs.

Background to the debate: In 2004, the United States Food and Drug Administration approved a radiofrequency identification (RFID) device that is implanted under the skin of the upper arm of patients and that stores the patient's medical identifier. When a scanner is passed over the device, the identifier is displayed on the screen of an RFID reader. An authorized health professional can then use the identifier to access the patient's clinical information, which is stored in a separate, secure database. Such RFID devices may have many medical benefits--such as expediting identification of patients and retrieval of their medical records. But critics of the technology have raised several concerns, including the risk of the patient's identifying information being used for nonmedical purposes.

STUDY OBJECTIVE: A key public health question is whether syndromic surveillance data provide early warning of infectious outbreaks. One cause for skepticism is that biological correlates of the administrative and clinical data used in these systems have not been rigorously assessed. This study measures the value of respiratory data currently used in syndromic surveillance systems to detect respiratory infections by comparing it against criterion standard viral testing within a pediatric population. METHODS: We conducted a longitudinal study with prospective validation in the emergency department (ED) of a tertiary care children's hospital. Children aged 7 years or younger who presented with a respiratory syndrome or who were tested for respiratory syncytial virus (RSV), influenza virus, parainfluenza virus, adenovirus, or enterovirus between January 1993 and June 2004 were included. We assessed the predictive ability of the viral tests by fitting generalized linear models to respiratory syndrome counts. RESULTS: Of 582,635 patient visits, 89,432 (15.4%) were for respiratory syndromes, and of these, 7,206 (8.1%) patients were tested for the viruses of interest. RSV was significantly related to respiratory syndrome counts (adjusted rate ratio [RR] 1.33; 95% confidence interval [CI] 1.04 to 1.71). In multivariate models including all viruses tested, influenza virus was also a significant predictor of respiratory syndrome counts (RR 1.47; 95% CI 1.03 to 2.10). This model accounted for 81.6% of the observed variability in respiratory syndrome counts. CONCLUSION: Respiratory syndromic surveillance data strongly correlate with virologic test results in a pediatric population, providing evidence of the biologic validity of such surveillance systems. Real-time outbreak detection systems relying on syndromic data may be an important adjunct to the current set of public health systems for the detection and surveillance of respiratory infections.

OBJECTIVE: To determine the accuracy of self-reported information from patients and families for use in a disease surveillance system. DESIGN: Patients and their parents presenting to the emergency department (ED) waiting room of an urban, tertiary care children's hospital were asked to use a Self-Report Tool, which consisted of a questionnaire asking questions related to the subjects' current illness. MEASUREMENTS: The sensitivity and specificity of three data sources for assigning patients to disease categories was measured: the ED chief complaint, physician diagnostic coding, and the completed Self-Report Tool. The gold standard metric for comparison was a medical record abstraction. RESULTS: A total of 936 subjects were enrolled. Compared to ED chief complaints, the Self-Report Tool was more than twice as sensitive in identifying respiratory illnesses (Rate ratio [RR]: 2.10, 95% confidence interval [CI] 1.81-2.44), and dermatological problems (RR: 2.23, 95% CI 1.56-3.17), as well as significantly more sensitive in detecting fever (RR: 1.90, 95% CI 1.67-2.17), gastrointestinal problems (RR: 1.10, 95% CI 1.00-1.20), and injuries (RR: 1.16, 95% CI 1.08-1.24). Sensitivities were also significantly higher when the Self-Report Tool performance was compared to diagnostic codes, with a sensitivity rate ratio of 4.42 (95% CI 3.45-5.68) for fever, 1.70 (95% CI 1.49-1.93) for respiratory problems, 1.15 (95% CI 1.04-1.27) for gastrointestinal problems, 2.02 (95% CI 1.42-2.87) for dermatologic problems, and 1.06 (95% CI 1.01-1.11) for injuries. CONCLUSIONS: Disease category assignment based on patient-reported information was significantly more sensitive in correctly identifying a disease category than data currently used by national and regional disease surveillance systems.

STUDY OBJECTIVES: We define the predictive value of parents' computer-based report for history and physical signs of dehydration for a primary outcome of percentage of dehydration (fluid deficit) and 2 secondary outcomes: clinically important acidosis and hospital admission. We also sought to compare the reports of physical signs related to dehydration made by parents and nurses. METHODS: We performed a prospective observational trial in an urban pediatric emergency department. A convenience sample of parents completed a computer-based interview covering historical details and physical signs (ill appearance, sunken fontanelle, sunken eyes, decreased tears, dry mouth, cool extremities, and weak cry) related to dehydration. Nurses independently completed an assessment of physical signs for enrolled children. The primary outcome was the degree of dehydration (fluid deficit), which was defined as the percentage difference between initial ED weight and stable final weight after the illness. Secondary outcomes included clinically important acidosis (defined as a serum CO(2) value of

Current efforts to detect covert bioterrorist attacks from increases in hospital visit rates are plagued by the unpredictable nature of these rates. Although many current systems evaluate hospital visit data 1 day at a time, we investigate evaluating multiple days at once to lessen the effects of this unpredictability and to improve both the timeliness and sensitivity of detection. To test this approach, we introduce simulated disease outbreaks of varying shapes, magnitudes, and durations into 10 years of historical daily visit data from a major tertiary-care metropolitan teaching hospital. We then investigate the effectiveness of using multiday temporal filters for detecting these simulated outbreaks within the noisy environment of the historical visit data. Our results show that compared with the standard 1-day approach, the multiday detection approach significantly increases detection sensitivity and decreases latency while maintaining a high specificity. We conclude that current biosurveillance systems should incorporate a wider temporal context to improve their effectiveness. Furthermore, for increased robustness and performance, hybrid systems should be developed to capitalize on the complementary strengths of different types of temporal filters.

Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the field and establishing collaborative networks for alert verification and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting.

OBJECTIVE: To improve identification of pertussis cases by developing a decision model that incorporates recent, local, population-level disease incidence. DESIGN: Retrospective cohort analysis of 443 infants tested for pertussis (2003-7). MEASUREMENTS: Three models (based on clinical data only, local disease incidence only, and a combination of clinical data and local disease incidence) to predict pertussis positivity were created with demographic, historical, physical exam, and state-wide pertussis data. Models were compared using sensitivity, specificity, area under the receiver-operating characteristics (ROC) curve (AUC), and related metrics. RESULTS: The model using only clinical data included cyanosis, cough for 1 week, and absence of fever, and was 89% sensitive (95% CI 79 to 99), 27% specific (95% CI 22 to 32) with an area under the ROC curve of 0.80. The model using only local incidence data performed best when the proportion positive of pertussis cultures in the region exceeded 10% in the 8-14 days prior to the infant's associated visit, achieving 13% sensitivity, 53% specificity, and AUC 0.65. The combined model, built with patient-derived variables and local incidence data, included cyanosis, cough for 1 week, and the variable indicating that the proportion positive of pertussis cultures in the region exceeded 10% 8-14 days prior to the infant's associated visit. This model was 100% sensitive (p0.04, 95% CI 92 to 100), 38% specific (p0.001, 95% CI 33 to 43), with AUC 0.82. CONCLUSIONS: Incorporating recent, local population-level disease incidence improved the ability of a decision model to correctly identify infants with pertussis. Our findings support fostering bidirectional exchange between public health and clinical practice, and validate a method for integrating large-scale public health datasets with rich clinical data to improve decision-making and public health.