RESUMEN
BACKGROUND: Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. OBJECTIVE: This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. METHOD: We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011-2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. RESULTS: We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. DISCUSSION: We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. CONCLUSION: Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.
RESUMEN
Traditional follow-up of patients with cardiovascular devices is still an activity that, in addition to serving an increasing population, requires a considerable amount of time and specialized human and technical resources. Our aim was to evaluate the applicability of the CareLink(®) (Medtronic, Minneapolis, MN) remote monitoring system as a complementary option to the follow-up of patients with implanted devices, between in-office visits. Evaluated outcomes included both clinical (event detection and time to diagnosis) and nonclinical (patient's satisfaction and economic costs) aspects. An observational, longitudinal, prospective study was conducted with patients from a Portuguese central hospital sampled by convenience during 1 week (43 patients). Data were collected in four moments: two in-office visits and two remote evaluations, reproducing 1 year of clinical follow-up. Data sources included health records, implant reports, initial demographic data collection, follow-up printouts, and a questionnaire. After selection criteria were verified, 15 patients (11 men [73%]) were included, 63.4±10.8 years old, representing 14.0±6.3 implant months. Clinically, 15 events were detected (9 by remote monitoring and 6 by patient-initiated activation), of which only 9 were symptomatic. We verified that remote monitoring could detect both symptomatic and asymptomatic events, whereas patient-initiated activation only detected symptomatic ones (p=0.028). Moreover, the mean diagnosis anticipation in patients with events was approximately 58 days (p<0.001). In nonclinical terms, we observed high or very high satisfaction (67% and 33%, respectively) with using remote monitoring technology, but still 8 patients (53%) stated they preferred in-office visits. Finally, the introduction of remote monitoring technology has the ability to reduce total follow-up costs for patients by 25%. We conclude that the use of this system constitutes a viable complementary option to the follow-up of patients with implantable devices, between in-office visits.