Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG–GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36–140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

We present a novel seismocardiography (SCG)based approach for real-time cardio-respiratory activity measurement called the Autocorrelated Differential Algorithm (ADA). Measurements were performed on ten male subjects in the supine position for three 7-minute-long sets each, corresponding to 14,619 heartbeats. The ADA utilized temporal variations, windowing, and autocorrelation to produce physiological measurements corresponding to heart rate (HR), and left ventricular ejection time, and estimations of respiration rate, volume, and phase. The versatility of the ADA was investigated in two contexts: physical exertion and heart rate variability. The accuracy of HR measurements at a sampling frequency of 200 Hz resulted in a correlation coefficient (r 2) of 0.9808 when compared with a manual annotation of all datasets. Its reproducibility was tested on externally obtained SCG and electrocardiography datasets, which produced an r 2 of 0.8224. The accuracy and computational time were also characterized by different sampling frequencies to quantify performance. The recommended sampling frequency is 200 Hz corresponding to a computation time of 0.05 s per instantaneous measurement using a standard desktop computer. The ADA delivered real-time SCG measurements with a refresh rate that was dependent on the computational time per measurement, which could be decreased by lowering the sampling frequency. The presented algorithm offers a novel tool toward real-time physiological monitoring in clinical and everyday scenarios.

BackgroundThe use of electronic health records (EHR) in clinical settings is considered pivotal to a patient-centered health care delivery system. However, uncertainty in cost recovery from EHR investments remains a significant concern in primary care practices.ObjectiveGuided by the question of “When implemented in primary care practices, what will be the return on investment (ROI) from an EHR implementation?”, the objectives of this study are two-fold: (1) to assess ROI from EHR in primary care practices and (2) to identify principal factors affecting the realization of positive ROI from EHR. We used a break-even point, that is, the time required to achieve cost recovery from an EHR investment, as an ROI indicator of an EHR investment.MethodsGiven the complexity exhibited by most EHR implementation projects, this study adopted a retrospective mixed-method research approach, particularly a multiphase study design approach. For this study, data were collected from community-based primary care clinics using EHR systems.ResultsWe collected data from 17 primary care clinics using EHR systems. Our data show that the sampled primary care clinics recovered their EHR investments within an average period of 10 months (95% CI 6.2-17.4 months), seeing more patients with an average increase of 27% in the active-patients-to-clinician-FTE (full time equivalent) ratio and an average increase of 10% in the active-patients-to-clinical-support-staff-FTE ratio after an EHR implementation. Our analysis suggests, with a 95% confidence level, that the increase in the number of active patients (P=.006), the increase in the active-patients-to-clinician-FTE ratio (P<.001), and the increase in the clinic net revenue (P<.001) are positively associated with the EHR implementation, likely contributing substantially to an average break-even point of 10 months.ConclusionsWe found that primary care clinics can realize a positive ROI with EHR. Our analysis of the variances in the time required to achieve cost recovery from EHR investments suggests that a positive ROI does not appear automatically upon implementing an EHR and that a clinic’s ability to leverage EHR for process changes seems to play a role. Policies that provide support to help primary care practices successfully make EHR-enabled changes, such as support of clinic workflow optimization with an EHR system, could facilitate the realization of positive ROI from EHR in primary care practices.

BackgroundPractice-based population health (PBPH) management is the proactive management of patients by their primary care clinical team. The ability of clinics to engage in PBPH and the means by which they incorporate it in a clinical setting remain unknown.ObjectiveWe conducted the Canadian Population Health Management Challenge to determine the capacity and preparedness of primary care settings to engage in PBPH using their existing medical record systems and to understand the complexities that may exist in PBPH implementation.MethodsWe recruited a sample of electronic medical record (EMR) -enabled and paper-based clinics from across Canada to participate in the challenge. The challenge required clinic staff and physicians to complete time-controlled, evidence-based practice reviews of their patients who may benefit from evidence-informed care, treatment, or interventions across five different areas (immunization, postmyocardial infarction care, cancer screening, diabetes management, and medication recall). We formulated a preparedness index to measure the capacity of clinics to engage in PBPH management. Finally, we conducted follow-up qualitative interviews to provide richer understanding of PBPH implementation and related issues (ie, challenges and facilitators).ResultsA total of 11 primary care clinics participated, representing 21 clinician practices. EMR-enabled clinics completed a full review of charts in an average of 1.37 hours. On the contrary, paper-based clinics reviewed nearly 10% of their charts in an average of 3.9 hours, hinting that they would have required an estimated 40 hours to complete a review of charts in their practice. Furthermore, the index revealed a major gap in preparedness between the EMR and paper-based clinics (0.86–3.78 vs 0.05–0.12), as well as a broad range among the EMR clinics. Finally, building on the results of the qualitative analysis, we identified factors facilitating the integration of PBPH.ConclusionsOur results suggest that EMR usage is pivotal in setting the foundation to support PBPH. The wide range of performance variation among EMR-enabled clinics suggests that EMR functionality and optimization, its support of clinical practice workflow, and policy issues to ensure adoption of standards are critical issues to facilitate PBPH.

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