Medical record search engines, using pseudonymised patient identity: an alternative to centralised medical records

Purpose The purpose of our multidisciplinary study was to define a pragmatic and secure alternative to the creation of a national centralised medical record which could gather together the different parts of the medical record of a patient scattered in the different hospitals where he was hospitalised without any risk of breaching confidentiality. Methods We first analyse the reasons for the failure and the dangers of centralisation (i.e. difficulty to define a European patients' identifier, to reach a common standard for the contents of the medical record, for data protection) and then propose an alternative that uses the existing available data on the basis that setting up a safe though imperfect system could be better than continuing a quest for a mythical perfect information system that we have still not found after a search that has lasted two decades. Results We describe the functioning of Medical Record Search Engines (MRSEs), using pseudonymisation of patients' identity. The MRSE will be able to retrieve and to provide upon an MD's request all the available information concerning a patient who has been hospitalised in different hospitals without ever having access to the patient's identity. The drawback of this system is that the medical practitioner then has to read all of the information and to create his own synthesis and eventually to reject extra data. Conclusions Faced with the difficulties and the risks of setting up a centralised medical record system, a system that gathers all of the available information concerning a patient could be of great interest. This low-cost pragmatic alternative which could be developed quickly should be taken into consideration by health authorities.

Centralised versus Decentralised Management of Patients's Medical Records

For more than 20 years, many countries have been trying to set up a standardised medical record at the regional or at the national level. Most of them have not reached this goal, essentially due to two main difficulties related to patient identification and medical records standardisation. Moreover, the issues raised by the centralisation of all gathered medical data have to be tackled particularly in terms of security and privacy. We discuss here the interest of a noncentralised management of medical records which would require a specific procedure that gives to the patient access to his/her distributed medical data, wherever he/she is located.

Privacy Preserving Probabilistic Record Linkage (P3RL): a novel method for linking existing health-related data and maintaining participant confidentiality.

BACKGROUND Record linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. A solution to protect privacy in probabilistic record linkages is to encrypt these sensitive information. Unfortunately, encrypted hash codes of two names differ completely if the plain names differ only by a single character. Therefore, standard encryption methods cannot be applied. To overcome these challenges, we developed the Privacy Preserving Probabilistic Record Linkage (P3RL) method. METHODS In this Privacy Preserving Probabilistic Record Linkage method we apply a three-party protocol, with two sites collecting individual data and an independent trusted linkage center as the third partner. Our method consists of three main steps: pre-processing, encryption and probabilistic record linkage. Data pre-processing and encryption are done at the sites by local personnel. To guarantee similar quality and format of variables and identical encryption procedure at each site, the linkage center generates semi-automated pre-processing and encryption templates. To retrieve information (i.e. data structure) for the creation of templates without ever accessing plain person identifiable information, we introduced a novel method of data masking. Sensitive string variables are encrypted using Bloom filters, which enables calculation of similarity coefficients. For date variables, we developed special encryption procedures to handle the most common date errors. The linkage center performs probabilistic record linkage with encrypted person identifiable information and plain non-sensitive variables. RESULTS In this paper we describe step by step how to link existing health-related data using encryption methods to preserve privacy of persons in the study. CONCLUSION Privacy Preserving Probabilistic Record linkage expands record linkage facilities in settings where a unique identifier is unavailable and/or regulations restrict access to the non-unique person identifiable information needed to link existing health-related data sets. Automated pre-processing and encryption fully protect sensitive information ensuring participant confidentiality. This method is suitable not just for epidemiological research but also for any setting with similar challenges.

Data Accuracy in Medical Record Abstraction

Clinical Research Data Quality Literature Review and Pooled Analysis We present a literature review and secondary analysis of data accuracy in clinical research and related secondary data uses. A total of 93 papers meeting our inclusion criteria were categorized according to the data processing methods. Quantitative data accuracy information was abstracted from the articles and pooled. Our analysis demonstrates that the accuracy associated with data processing methods varies widely, with error rates ranging from 2 errors per 10,000 files to 5019 errors per 10,000 fields. Medical record abstraction was associated with the highest error rates (70–5019 errors per 10,000 fields). Data entered and processed at healthcare facilities had comparable error rates to data processed at central data processing centers. Error rates for data processed with single entry in the presence of on-screen checks were comparable to double entered data. While data processing and cleaning methods may explain a significant amount of the variability in data accuracy, additional factors not resolvable here likely exist. Defining Data Quality for Clinical Research: A Concept Analysis Despite notable previous attempts by experts to define data quality, the concept remains ambiguous and subject to the vagaries of natural language. This current lack of clarity continues to hamper research related to data quality issues. We present a formal concept analysis of data quality, which builds on and synthesizes previously published work. We further posit that discipline-level specificity may be required to achieve the desired definitional clarity. To this end, we combine work from the clinical research domain with findings from the general data quality literature to produce a discipline-specific definition and operationalization for data quality in clinical research. While the results are helpful to clinical research, the methodology of concept analysis may be useful in other fields to clarify data quality attributes and to achieve operational definitions. Medical Record Abstractor’s Perceptions of Factors Impacting the Accuracy of Abstracted Data Medical record abstraction (MRA) is known to be a significant source of data errors in secondary data uses. Factors impacting the accuracy of abstracted data are not reported consistently in the literature. Two Delphi processes were conducted with experienced medical record abstractors to assess abstractor’s perceptions about the factors. The Delphi process identified 9 factors that were not found in the literature, and differed with the literature by 5 factors in the top 25%. The Delphi results refuted seven factors reported in the literature as impacting the quality of abstracted data. The results provide insight into and indicate content validity of a significant number of the factors reported in the literature. Further, the results indicate general consistency between the perceptions of clinical research medical record abstractors and registry and quality improvement abstractors. Distributed Cognition Artifacts on Clinical Research Data Collection Forms Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Distributed cognition in medical record abstraction has not been studied as a possible explanation for abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.

Electronic medical record systems, data quality and loss to follow-up: survey of antiretroviral therapy programmes in resource-limited settings

OBJECTIVE: To describe the electronic medical databases used in antiretroviral therapy (ART) programmes in lower-income countries and assess the measures such programmes employ to maintain and improve data quality and reduce the loss of patients to follow-up. METHODS: In 15 countries of Africa, South America and Asia, a survey was conducted from December 2006 to February 2007 on the use of electronic medical record systems in ART programmes. Patients enrolled in the sites at the time of the survey but not seen during the previous 12 months were considered lost to follow-up. The quality of the data was assessed by computing the percentage of missing key variables (age, sex, clinical stage of HIV infection, CD4+ lymphocyte count and year of ART initiation). Associations between site characteristics (such as number of staff members dedicated to data management), measures to reduce loss to follow-up (such as the presence of staff dedicated to tracing patients) and data quality and loss to follow-up were analysed using multivariate logit models. FINDINGS: Twenty-one sites that together provided ART to 50 060 patients were included (median number of patients per site: 1000; interquartile range, IQR: 72-19 320). Eighteen sites (86%) used an electronic database for medical record-keeping; 15 (83%) such sites relied on software intended for personal or small business use. The median percentage of missing data for key variables per site was 10.9% (IQR: 2.0-18.9%) and declined with training in data management (odds ratio, OR: 0.58; 95% confidence interval, CI: 0.37-0.90) and weekly hours spent by a clerk on the database per 100 patients on ART (OR: 0.95; 95% CI: 0.90-0.99). About 10 weekly hours per 100 patients on ART were required to reduce missing data for key variables to below 10%. The median percentage of patients lost to follow-up 1 year after starting ART was 8.5% (IQR: 4.2-19.7%). Strategies to reduce loss to follow-up included outreach teams, community-based organizations and checking death registry data. Implementation of all three strategies substantially reduced losses to follow-up (OR: 0.17; 95% CI: 0.15-0.20). CONCLUSION: The quality of the data collected and the retention of patients in ART treatment programmes are unsatisfactory for many sites involved in the scale-up of ART in resource-limited settings, mainly because of insufficient staff trained to manage data and trace patients lost to follow-up.

Patient and physician characteristics as predictors for consent to participate in an electronic medical record study

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Patient and physician characteristics as predictors for consent to participate in an electronic medical record study

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Patient data sharing for the improvement of healthcare and medical research: towards a moral duty of patients to share health-related data?

The project answers to the following central research question: ‘How would a moral duty of patients to transfer (health) data for the benefit of health care improvement, research, and public health in the eHealth sector sit within the existing confidentiality, privacy, and data protection legislations?’. The improvement of healthcare services, research, and public health relies on patient data, which is why one might raise the question concerning a potential moral responsibility of patients to transfer data concerning health. Such a responsibility logically would have subsequent consequences for care providers concerning the further transferring of health data with other healthcare providers or researchers and other organisations (who also possibly transfer the data further with others and other organisations). Otherwise, the purpose of the patients’ moral duty, i.e. to improve the care system and research, would be undermined. Albeit the arguments that may exist in favour of a moral responsibility of patients to share health-related data, there are also some moral hurdles that come with such a moral responsibility. Furthermore, the existing European and national confidentiality, privacy and data protection legislations appear to hamper such a possible moral duty, and they may need to be reconsidered to unlock the full use of data for healthcare and research.

Webinar Training: an acceptable, feasible and effective approach for multi-site medical record abstraction: the BOWII experience

Background Abstractor training is a key element in creating valid and reliable data collection procedures. The choice between in-person vs. remote or simultaneous vs. sequential abstractor training has considerable consequences for time and resource utilization. We conducted a web-based (webinar) abstractor training session to standardize training across six individual Cancer Research Network (CRN) sites for a study of breast cancer treatment effects in older women (BOWII). The goals of this manuscript are to describe the training session, its participants and participants' evaluation of webinar technology for abstraction training. Findings A webinar was held for all six sites with the primary purpose of simultaneously training staff and ensuring consistent abstraction across sites. The training session involved sequential review of over 600 data elements outlined in the coding manual in conjunction with the display of data entry fields in the study's electronic data collection system. Post-training evaluation was conducted via Survey Monkey©. Inter-rater reliability measures for abstractors within each site were conducted three months after the commencement of data collection. Ten of the 16 people who participated in the training completed the online survey. Almost all (90%) of the 10 trainees had previous medical record abstraction experience and nearly two-thirds reported over 10 years of experience. Half of the respondents had previously participated in a webinar, among which three had participated in a webinar for training purposes. All rated the knowledge and information delivered through the webinar as useful and reported it adequately prepared them for data collection. Moreover, all participants would recommend this platform for multi-site abstraction training. Consistent with participant-reported training effectiveness, results of data collection inter-rater agreement within sites ranged from 89 to 98%, with a weighted average of 95% agreement across sites. Conclusions Conducting training via web-based technology was an acceptable and effective approach to standardizing medical record review across multiple sites for this group of experienced abstractors. Given the substantial time and cost savings achieved with the webinar, coupled with participants' positive evaluation of the training session, researchers should consider this instructional method as part of training efforts to ensure high quality data collection in multi-site studies.

Notification of abnormal lab test results in an electronic medical record: do any safety concerns remain?

BACKGROUND: Follow-up of abnormal outpatient laboratory test results is a major patient safety concern. Electronic medical records can potentially address this concern through automated notification. We examined whether automated notifications of abnormal laboratory results (alerts) in an integrated electronic medical record resulted in timely follow-up actions. METHODS: We studied 4 alerts: hemoglobin A1c > or =15%, positive hepatitis C antibody, prostate-specific antigen > or =15 ng/mL, and thyroid-stimulating hormone > or =15 mIU/L. An alert tracking system determined whether the alert was acknowledged (ie, provider clicked on and opened the message) within 2 weeks of transmission; acknowledged alerts were considered read. Within 30 days of result transmission, record review and provider contact determined follow-up actions (eg, patient contact, treatment). Multivariable logistic regression models analyzed predictors for lack of timely follow-up. RESULTS: Between May and December 2008, 78,158 tests (hemoglobin A1c, hepatitis C antibody, thyroid-stimulating hormone, and prostate-specific antigen) were performed, of which 1163 (1.48%) were transmitted as alerts; 10.2% of these (119/1163) were unacknowledged. Timely follow-up was lacking in 79 (6.8%), and was statistically not different for acknowledged and unacknowledged alerts (6.4% vs 10.1%; P =.13). Of 1163 alerts, 202 (17.4%) arose from unnecessarily ordered (redundant) tests. Alerts for a new versus known diagnosis were more likely to lack timely follow-up (odds ratio 7.35; 95% confidence interval, 4.16-12.97), whereas alerts related to redundant tests were less likely to lack timely follow-up (odds ratio 0.24; 95% confidence interval, 0.07-0.84). CONCLUSIONS: Safety concerns related to timely patient follow-up remain despite automated notification of non-life-threatening abnormal laboratory results in the outpatient setting.

Using Informatics and the Electronic Medical Record to Describe Antimicrobial Use in the Clinical Management of Diarrhea Cases at 12 Companion Animal Practices

Antimicrobial drugs may be used to treat diarrheal illness in companion animals. It is important to monitor antimicrobial use to better understand trends and patterns in antimicrobial resistance. There is no monitoring of antimicrobial use in companion animals in Canada. To explore how the use of electronic medical records could contribute to the ongoing, systematic collection of antimicrobial use data in companion animals, anonymized electronic medical records were extracted from 12 participating companion animal practices and warehoused at the University of Calgary. We used the pre-diagnostic, clinical features of diarrhea as the case definition in this study. Using text-mining technologies, cases of diarrhea were described by each of the following variables: diagnostic laboratory tests performed, the etiological diagnosis and antimicrobial therapies. The ability of the text miner to accurately describe the cases for each of the variables was evaluated. It could not reliably classify cases in terms of diagnostic tests or etiological diagnosis; a manual review of a random sample of 500 diarrhea cases determined that 88/500 (17.6%) of the target cases underwent diagnostic testing of which 36/88 (40.9%) had an etiological diagnosis. Text mining, compared to a human reviewer, could accurately identify cases that had been treated with antimicrobials with high sensitivity (92%, 95% confidence interval, 88.1%-95.4%) and specificity (85%, 95% confidence interval, 80.2%-89.1%). Overall, 7400/15,928 (46.5%) of pets presenting with diarrhea were treated with antimicrobials. Some temporal trends and patterns of the antimicrobial use are described. The results from this study suggest that informatics and the electronic medical records could be useful for monitoring trends in antimicrobial use.

Intra-Rater and Inter-Rater Reliability of a Medical Record Abstraction Study on Transition of Care after Childhood Cancer.

BACKGROUND The abstraction of data from medical records is a widespread practice in epidemiological research. However, studies using this means of data collection rarely report reliability. Within the Transition after Childhood Cancer Study (TaCC) which is based on a medical record abstraction, we conducted a second independent abstraction of data with the aim to assess a) intra-rater reliability of one rater at two time points; b) the possible learning effects between these two time points compared to a gold-standard; and c) inter-rater reliability. METHOD Within the TaCC study we conducted a systematic medical record abstraction in the 9 Swiss clinics with pediatric oncology wards. In a second phase we selected a subsample of medical records in 3 clinics to conduct a second independent abstraction. We then assessed intra-rater reliability at two time points, the learning effect over time (comparing each rater at two time-points with a gold-standard) and the inter-rater reliability of a selected number of variables. We calculated percentage agreement and Cohen's kappa. FINDINGS For the assessment of the intra-rater reliability we included 154 records (80 for rater 1; 74 for rater 2). For the inter-rater reliability we could include 70 records. Intra-rater reliability was substantial to excellent (Cohen's kappa 0-6-0.8) with an observed percentage agreement of 75%-95%. In all variables learning effects were observed. Inter-rater reliability was substantial to excellent (Cohen's kappa 0.70-0.83) with high agreement ranging from 86% to 100%. CONCLUSIONS Our study showed that data abstracted from medical records are reliable. Investigating intra-rater and inter-rater reliability can give confidence to draw conclusions from the abstracted data and increase data quality by minimizing systematic errors.

Low correlation between self-report and medical record documentation of urinary tract infection symptoms.

BACKGROUND Correlations between symptom documentation in medical records and patient self-report (SR) vary depending on the condition studied. Patient symptoms are particularly important in urinary tract infection (UTI) diagnosis, and this correlation for UTI symptoms is currently unknown. METHODS This is a cross-sectional survey study in hospitalized patients with Escherichia coli bacteriuria. Patients were interviewed within 24 hours of diagnosis for the SR of UTI symptoms. We reviewed medical records for UTI symptoms documented by admitting or treating inpatient physicians (IPs), nurses (RNs), and emergency physicians (EPs). The level of agreement between groups was assessed using Cohen κ coefficient. RESULTS Out of 43 patients, 34 (79%) self-reported at least 1 of 6 primary symptoms. The most common self-reported symptoms were urinary frequency (53.5%); retention (41.9%); flank pain, suprapubic pain, and fatigue (37.2% each); and dysuria (30.2%). Correlation between SR and medical record documentation was slight to fair (κ, 0.06-0.4 between SR and IPs and 0.09-0.5 between SR and EDs). Positive agreement was highest for dysuria and frequency. CONCLUSION Correlation between self-reported UTI symptoms and health care providers' documentation was low to fair. Because medical records are a vital source of information for clinicians and researchers and symptom assessment and documentation are vital in distinguishing UTI from asymptomatic bacteriuria, efforts must be made to improve documentation.