279 research outputs found
An iOS Framework for the Indivo X Personally Controlled Health Record
The Indivo X personally controlled health record creates a channel between researchers and the patient/subject in several large scale projects. Indivo enables patients to access their health data through a web interface and, as an āapps platformā, can be extended in functionality. Patient-facing apps, such as a medication list, may improve the data flow between researcher and patient, in both directions, and as such provide better data for the researcher and immediate benefit for the patient. However, research projects in general do not allocate large funds to patient facing apps, let alone a mobile interface. Thus we have created a framework that greatly simplifies connecting an iOS app to an Indivo X server. Our open-source framework enables novel as well as experienced iOS developers to build mobile interfaces for their research subjects, taking advantage of Indivo X
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Surveillance of medication use: early identification of poor adherence
Background: We sought to measure population-level adherence to antihyperlipidemics, antihypertensives, and oral hypoglycemics, and to develop a model for early identification of subjects at high risk of long-term poor adherence. Methods Prescription-filling data for 2 million subjects derived from a payor's insurance claims were used to evaluate adherence to three chronic drugs over 1 year. We relied on patterns of prescription fills, including the length of gaps in medication possession, to measure adherence among subjects and to build models for predicting poor long-term adherence. Results: All prescription fills for a specific drug were sequenced chronologically into drug eras. 61.3% to 66.5% of the prescription patterns contained medication gaps >30 days during the first year of drug use. These interrupted drug eras include long-term discontinuations, where the subject never again filled a prescription for any drug in that category in the dataset, which represent 23.7% to 29.1% of all drug eras. Among the prescription-filling patterns without large medication gaps, 0.8% to 1.3% exhibited long-term poor adherence. Our models identified these subjects as early as 60 days after the first prescription fill, with an area under the curve (AUC) of 0.81. Model performance improved as the predictions were made at later time-points, with AUC values increasing to 0.93 at the 120-day time-point. Conclusions: Dispensed medication histories (widely available in real time) are useful for alerting providers about poorly adherent patients and those who will be non-adherent several months later. Efforts to use these data in point of care and decision support facilitating patient are warranted
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App Store for EHRs and Patients Both
The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project ( www.smartplatforms.org ) seeks to develop an iPhone-like health information technology platform with substitutable apps constructed around core services. It is funded by a grant from the Office of the National Coordinator of Health Information Technologyās Strategic Health IT Advanced Research Projects (SHARP) Program. SMART technologies enable existing electronic health records and HIT platforms to run substitutable apps. Substitutability is the capability inherent in a system of replacing one application with another of similar functionality. We created a patient-facing SMART instance using the open source Indivo personally controlled health record (PCHR). The SMART āread-onlyā API has been deployed on multiple systems, including the Cerner installation at Boston Childrenās Hospital and the World Vista EHR. We sought to SMART-enable Indivo, the open source reference PCHR upon which HealthVault and other PCHRs were modeled. PCHRs provide patients with a secure repository of their health information that can be exposed to apps across a programming interface. We updated the open source Indivo PCHR to support the SMART API, enabling Indivo to act as a patient-facing apps platform, running the same or similar versions of apps that face clinicians
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Surveillance of an Online Social Network to Assess Population-level Diabetes Health Status and Healthcare Quality
Objective: Test a novel health monitoring approach by engaging an international online diabetes social network (SN) in consented health surveillance. Methods: Collection of structured self-reports about preventive and self-care practices and health status using a software application (āappā) that supports SN-mediated health research. Comparison of SN measures by diabetes type; and, SN with Behavioral Risk Factor Surveillance System (BRFSS) data, for US-residing insulin dependent respondents, using logistic regression. Results: Of 2,414 SN app users, 82% (n=1979) provided an A1c and 41% (n=996) completed a care survey of which 931 have diabetes. Of these: 65% and 41% were immunized against influenza and pneumonia respectively, 90% had their cholesterol checked, 82% and 66%, had their eyes and feet checked, respectively. Type 1/LADA respondents were more likely than Type 2/pre-diabetic respondents to report all five recommended practices (Adjusted OR (95% CI) 2.2 (1.5, 3.2)). Past year self-care measures were: 58% self-monitored their blood glucose (SMBG) ā„ 5 times daily, 37% saw their diabetes nutritionist, 56% saw a diabetes nurse educator, 53% saw a doctor for their diabetes ā„ 4 times. Reports of health status did not differ by diabetes type in the SN sample. The SN group was more likely than the BRFSS comparator group to use all five preventive care practices (Adjusted OR (95% CI) 1.8 (1.4, 2.1) and SMBG ā„ 5 times daily (Adjusted OR (95% CI) 10.1 (6.8, 14.9). Conclusions: Rapid assessment of diabetes care practices using a novel, SN-mediated approach can extend the capability of standard health surveillance systems
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Electronic Patient-Physician Communication: Problems and Promise
A critical mass of Internet users will soon enable wide diffusion of electronic communication within medical practice. E-mail between physicians and patients offers important opportunities for better communication. Linking patients and physicians through e-mail may increase the involvement of patients in supervising and documenting their own health care, processes that may activate patients and contribute to improved health. These new linkages may have profound implications for the patientphysician relationship. Although the federal government proposes regulation of telemedicine technologies and
medical software, communications technologies are evolving under less scrutiny. Unless these technologies are implemented with substantial forethought, they may disturb delicate balances in the patient-physician relationship, widen social disparities in health outcomes, and create barriers to access to health care.
This paper seeks to identify the promise and pitfalls of electronic patient-pbysician communication before such technology becomes widely distributed. A research agenda is proposed that would provide data that are useful for careful shaping of the communications infrastructure. The paper addresses the need to 1) define appropriate use of the various modes of patient-physician communication, 2) ensure the security and confidentiality of patient information, 3) create user interfaces that guide patients in effective use of the technology, 4) proactively assess medicolegal liability, and 5) ensure access to the technology by a multicultural, multilingual population with varying degrees of literacy.History of Scienc
Premarket Safety and Efficacy Studies for ADHD Medications in Children
Background: Attention-deficit hyperactivity disorder (ADHD) is a chronic condition and pharmacotherapy is the mainstay of treatment, with a variety of ADHD medications available to patients. However, it is unclear to what extent the long-term safety and efficacy of ADHD drugs have been evaluated prior to their market authorization. We aimed to quantify the number of participants studied and their length of exposure in ADHD drug trials prior to marketing. Methods: We identified all ADHD medications approved by the Food and Drug Administration (FDA) and extracted data on clinical trials performed by the sponsor and used by the FDA to evaluate the drugās clinical efficacy and safety. For each ADHD medication, we measured the total number of participants studied and the length of participant exposure and identified any FDA requests for post-marketing trials. Results: A total of 32 clinical trials were conducted for the approval of 20 ADHD drugs. The median number of participants studied per drug was 75 (IQR 0, 419). Eleven drugs (55%) were approved after <100 participants were studied and 14 (70%) after <300 participants. The median trial length prior to approval was 4 weeks (IQR 2, 9), with 5 (38%) drugs approved after participants were studied <4 weeks and 10 (77%) after <6 months. Six drugs were approved with requests for specific additional post-marketing trials, of which 2 were performed. Conclusions: Clinical trials conducted for the approval of many ADHD drugs have not been designed to assess rare adverse events or long-term safety and efficacy. While post-marketing studies can fill in some of the gaps, better assurance is needed that the proper trials are conducted either before or after a new medication is approved
Real time spatial cluster detection using interpoint distances among precise patient locations
BACKGROUND: Public health departments in the United States are beginning to gain timely access to health data, often as soon as one day after a visit to a health care facility. Consequently, new approaches to outbreak surveillance are being developed. When cases cluster geographically, an analysis of their spatial distribution can facilitate outbreak detection. Our method focuses on detecting perturbations in the distribution of pair-wise distances among all patients in a geographical region. Barring outbreaks, this distribution can be quite stable over time. We sought to exemplify the method by measuring its cluster detection performance, and to determine factors affecting sensitivity to spatial clustering among patients presenting to hospital emergency departments with respiratory syndromes. METHODS: The approach was to (1) define a baseline spatial distribution of home addresses for a population of patients visiting an emergency department with respiratory syndromes using historical data; (2) develop a controlled feature set simulation by inserting simulated outbreak data with varied parameters into authentic background noise, thereby creating semisynthetic data; (3) compare the observed with the expected spatial distribution; (4) establish the relative value of different alarm strategies so as to maximize sensitivity for the detection of clustering; and (5) measure factors which have an impact on sensitivity. RESULTS: Overall sensitivity to detect spatial clustering was 62%. This contrasts with an overall alarm rate of less than 5% for the same number of extra visits when the extra visits were not characterized by geographic clustering. Clusters that produced the least number of alarms were those that were small in size (10 extra visits in a week, where visits per week ranged from 120 to 472), diffusely distributed over an area with a 3 km radius, and located close to the hospital (5 km) in a region most densely populated with patients to this hospital. Near perfect alarm rates were found for clusters that varied on the opposite extremes of these parameters (40 extra visits, within a 250 meter radius, 50 km from the hospital). CONCLUSION: Measuring perturbations in the interpoint distance distribution is a sensitive method for detecting spatial clustering. When cases are clustered geographically, there is clearly power to detect clustering when the spatial distribution is represented by the M statistic, even when clusters are small in size. By varying independent parameters of simulated outbreaks, we have demonstrated empirically the limits of detection of different types of outbreaks
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