Related Articles

Objectives

Delayed diagnosis of colorectal cancer (CRC) is among the most common reasons for ambulatory diagnostic malpractice claims in the United States. Our objective was to describe missed opportunities to diagnose CRC before endoscopic referral, in terms of patient characteristics, nature of clinical clues, and types of diagnostic-process breakdowns involved.

Methods

We conducted a retrospective cohort study of consecutive, newly diagnosed cases of CRC between February 1999 and June 2007 at a tertiary health-care system in Texas. Two reviewers independently evaluated the electronic record of each patient using a standardized pretested data collection instrument. Missed opportunities were defined as care episodes in which endoscopic evaluation was not initiated despite the presence of one or more clues that warrant a diagnostic workup for CRC. Predictors of missed opportunities were evaluated in logistic regression. The types of breakdowns involved in the diagnostic process were also determined and described.

Results

Of the 513 patients with CRC who met the inclusion criteria, both reviewers agreed on the presence of at least one missed opportunity in 161 patients. Among these patients there was a mean of 4.2 missed opportunities and 5.3 clues. The most common clues were suspected or confirmed iron deficiency anemia, positive fecal occult blood test, and hematochezia. The odds of a missed opportunity were increased in patients older than 75 years (odds ratio (OR) = 2.3; 95% confidence interval (CI) 1.3–4.1) or with iron deficiency anemia (OR = 2.2; 95% CI 1.3–3.6), whereas the odds of a missed opportunity were lower in patients with abnormal flexible sigmoidoscopy (OR = 0.06; 95% CI 0.01–0.51), or imaging suspicious for CRC (OR = 0.3; 95% CI 0.1–0.9). Anemia was the clue associated with the longest time to endoscopic referral (median = 393 days). Most process breakdowns occurred in the provider–patient clinical encounter and in the follow-up of patients or abnormal diagnostic test results.

Conclusions

Missed opportunities to initiate workup for CRC are common despite the presence of many clues suggestive of CRC diagnosis. Future interventions are needed to reduce the process breakdowns identified.

Background

A cohort of colorectal cancer (CRC) patients represents an opportunity to study missed opportunities for earlier diagnosis. Primary objective: To study the epidemiology of diagnostic delays and failures to offer/complete CRC screening. Secondary objective: To identify system- and patient-related factors that may contribute to diagnostic delays or failures to offer/complete CRC screening.

Methods

Setting: Rural Veterans Administration (VA) Healthcare system. Participants: CRC cases diagnosed within the VA between 1/1/2000 and 3/1/2007. Data sources: progress notes, orders, and pathology, laboratory, and imaging results obtained between 1/1/1995 and 12/31/2007. Completed CRC screening was defined as a fecal occult blood test or flexible sigmoidoscopy (both within five years), or colonoscopy (within 10 years); delayed diagnosis was defined as a gap of more than six months between an abnormal test result and evidence of clinician response. A summary abstract of the antecedent clinical care for each patient was created by a certified gastroenterologist (GI), who jointly reviewed and coded the abstracts with a general internist (TW).

Results

The study population consisted of 150 CRC cases that met the inclusion criteria. The mean age was 69.04 (range 35-91); 99 (66%) were diagnosed due to symptoms; 61 cases (46%) had delays associated with system factors; of them, 57 (38% of the total) had delayed responses to abnormal findings. Fifteen of the cases (10%) had prompt symptom evaluations but received no CRC screening; no patient factors were identified as potentially contributing to the failure to screen/offer to screen. In total, 97 (65%) of the cases had missed opportunities for early diagnosis and 57 (38%) had patient factors that likely contributed to the diagnostic delay or apparent failure to screen/offer to screen.

Conclusion

Missed opportunities for earlier CRC diagnosis were frequent. Additional studies of clinical data management, focusing on following up abnormal findings, and offering/completing CRC screening, are needed.

Electronic health record (EHR) systems offer an exceptional opportunity for studying many diseases and their associated medical conditions within a population. The increasing number of clinical record entries that have become available electronically provides access to rich, large sets of patients' longitudinal medical information. By integrating and comparing relations found in the EHRs with those already reported in the literature, we are able to verify existing and to identify rare or novel associations. Of particular interest is the identification of rare disease co-morbidities, where the small numbers of diagnosed patients make robust statistical analysis difficult. Here, we introduce ADAMS, an Application for Discovering Disease Associations using Multiple Sources, which contains various statistical and language processing operations. We apply ADAMS to the New York-Presbyterian Hospital's EHR to combine the information from the relational diagnosis tables and textual discharge summaries with those from PubMed and Wikipedia in order to investigate the co-morbidities of the rare diseases Kaposi sarcoma, toxoplasmosis, and Kawasaki disease. In addition to finding well-known characteristics of diseases, ADAMS can identify rare or previously unreported associations. In particular, we report a statistically significant association between Kawasaki disease and diagnosis of autistic disorder.

Background

Identification of negation in electronic health records is essential if we are to understand the computable meaning of the records: Our objective is to compare the accuracy of an automated mechanism for assignment of Negation to clinical concepts within a compositional expression with Human Assigned Negation. Also to perform a failure analysis to identify the causes of poorly identified negation (i.e. Missed Conceptual Representation, Inaccurate Conceptual Representation, Missed Negation, Inaccurate identification of Negation).

Methods

41 Clinical Documents (Medical Evaluations; sometimes outside of Mayo these are referred to as History and Physical Examinations) were parsed using the Mayo Vocabulary Server Parsing Engine. SNOMED-CT™ was used to provide concept coverage for the clinical concepts in the record. These records resulted in identification of Concepts and textual clues to Negation. These records were reviewed by an independent medical terminologist, and the results were tallied in a spreadsheet. Where questions on the review arose Internal Medicine Faculty were employed to make a final determination.

Results

SNOMED-CT was used to provide concept coverage of the 14,792 Concepts in 41 Health Records from John's Hopkins University. Of these, 1,823 Concepts were identified as negative by Human review. The sensitivity (Recall) of the assignment of negation was 97.2% (p < 0.001, Pearson Chi-Square test; when compared to a coin flip). The specificity of assignment of negation was 98.8%. The positive likelihood ratio of the negation was 81. The positive predictive value (Precision) was 91.2%

Conclusion

Automated assignment of negation to concepts identified in health records based on review of the text is feasible and practical. Lexical assignment of negation is a good test of true Negativity as judged by the high sensitivity, specificity and positive likelihood ratio of the test. SNOMED-CT had overall coverage of 88.7% of the concepts being negated.

Abstract Objective: To investigate factors that determine the feasibility and effectiveness of a critiquing system for asthma/COPD that will be integrated with a general practitioner's (GP's) information system.

Design: A simulation study. Four reviewers, playing the role of the computer, generated critiquing comments and requests for additional information on six electronic medical records of patients with asthma/COPD. Three GPs who treated the patients, playing users, assessed the comments and provided missing information when requested. The GPs were asked why requested missing information was unavailable. The reviewers reevaluated their comments after receiving requested missing information.

Measurements: Descriptions of the number and nature of critiquing comments and requests for missing information. Assessment by the GPs of the critiquing comments in terms of agreement with each comment and judgment of its relevance, both on a five-point scale. Analysis of causes for the (un-)availability of requested missing information. Assessment of the impact of missing information on the generation of critiquing comments.

Results: Four reviewers provided 74 critiquing comments on 87 visits in six medical records. Most were about prescriptions (n = 28) and the GPs' workplans (n = 27). The GPs valued comments about diagnostics the most. The correlation between the GPs' agreement and relevance scores was 0.65. However, the GPs' agreements with prescription comments (complete disagreement, 31.3%; disagreement, 20.0%; neutral, 13.8%; agreement, 17.5%; complete agreement, 17.5%) differed from their judgments of these comments' relevance (completely irrelevant, 9.0%; irrelevant, 24.4%; neutral, 24.4%; relevant, 32.1%; completely relevant, 10.3%). The GPs were able to provide answers to 64% of the 90 requests for missing information. Reasons available information had not been recorded were: the GPs had not recorded the information explicitly; they had assumed it to be common knowledge; it was available elsewhere in the record. Reasons information was unavailable were: the decision had been made by another; the GP had not recorded the information. The reviewers left 74% of the comments unchanged after receiving requested missing information.

Conclusion: Human reviewers can generate comments based on information currently available in electronic medical records of patients with asthma/COPD. The GPs valued comments regarding the diagnostic process the most. Although they judged prescription comments relevant, they often strongly disagreed with them, a discrepancy that poses a challenge for the presentation of critiquing comments for the future critiquing system. Requested additional information that was provided by the GPs led to few changes. Therefore, as system developers faced with the decision to build an integrated, non-inquisitive or an inquisitive critiquing system, the authors choose the former.

Background

Predictors of antiretroviral treatment (ART) failure are not well characterized for heterogeneous clinic populations.

Methods

A retrospective analysis was conducted of HIV-infected patients followed in an urban HIV clinic with an HIV RNA measurement ≤400 copies/mL on ART between January 1, 2003, and December 31, 2004. The primary endpoint was treatment failure, defined as virologic failure (≥1 HIV RNA measurement >400 copies/mL), unsanctioned stopping of ART, or loss to follow-up. Prior ART adherence and other baseline patient characteristics, determined at the time of the first suppressed HIV RNA load on or after January 1, 2003, were extracted from the electronic health record (EHR). Predictors of failure were assessed using proportional hazards modeling.

Results

Of 829 patients in the clinic, 614 had at least 1 HIV RNA measurement ≤400 copies/mL during the study period. Of these, 167 (27.2%) experienced treatment failure. Baseline characteristics associated with treatment failure in the multivariate model were: poor adherence (hazard ratio [HR] = 3.44; 95% confidence interval [CI]: 2.34 to 5.05), absolute neutrophil count <1000/mm3 (HR = 2.90, 95% CI: 1.26 to 6.69), not suppressed on January 1, 2003 (HR = 2.69, 95% CI: 1.78 to 4.07) or <12 months of suppression (HR = 1.64, 95% CI: 1.10 to 2.45), CD4 count <200 cells/mm3 (HR = 1.90, 95% CI: 1.31 to 2.76), nucleoside-only regimen (HR = 1.75, 95% CI: 1.08 to 2.82), prior virologic failure (HR = 1.70, 95% CI: 1.22 to 2.39) and ≥1 missed visit in the prior year (HR = 1.56, 95% CI: 1.13 to 2.16).

Conclusions

More than one quarter of patients in a heterogeneous clinic population had treatment failure over a 2-year period. Prior ART adherence and other EHR data readily identify patient characteristics that could trigger specific interventions to improve ART outcomes.

Background

The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining?

Results

We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. aFor text mining, preprocessing the EHR corpus with fingerprinting yields significantly better results.

Conclusions

Before applying text-mining techniques, one must pay careful attention to the structure of the analyzed corpora. While the importance of data cleaning has been known for low-level text characteristics (e.g., encoding and spelling), high-level and difficult-to-quantify corpus characteristics, such as naturally occurring redundancy, can also hurt text mining. Fingerprinting enables text-mining techniques to leverage available data in the EHR corpus, while avoiding the bias introduced by redundancy.

Background

Trauma tertiary surveys (TTS) are advocated to reduce the rate of missed injuries in hospitalized trauma patients. Moreover, the missed injury rate can be a quality indicator of trauma care performance. Current variation of the definition of missed injury restricts interpretation of the effect of the TTS and limits the use of missed injury for benchmarking. Only a few studies have specifically assessed the effect of the TTS on missed injury. We aimed to systematically appraise these studies using outcomes of two common definitions of missed injury rates and long-term health outcomes.

Methods

A systematic review was performed. An electronic search (without language or publication restrictions) of the Cochrane Library, Medline and Ovid was used to identify studies assessing TTS with short-term measures of missed injuries and long-term health outcomes. ‘Missed injury’ was defined as either: Type I) any injury missed at primary and secondary survey and detected by the TTS; or Type II) any injury missed at primary and secondary survey and missed by the TTS, detected during hospital stay. Two authors independently selected studies. Risk of bias for observational studies was assessed using the Newcastle-Ottawa scale.

Results

Ten observational studies met our inclusion criteria. None was randomized and none reported long-term health outcomes. Their risk of bias varied considerably. Nine studies assessed Type I missed injury and found an overall rate of 4.3%. A single study reported Type II missed injury with a rate of 1.5%. Three studies reported outcome data on missed injuries for both control and intervention cohorts, with two reporting an increase in Type I missed injuries (3% vs. 7%, P<0.01), and one a decrease in Type II missed injuries (2.4% vs. 1.5%, P=0.01).

Conclusions

Overall Type I and Type II missed injury rates were 4.3% and 1.5%. Routine TTS performance increased Type I and reduced Type II missed injuries. However, evidence is sub-optimal: few observational studies, non-uniform outcome definitions and moderate risk of bias. Future studies should address these issues to allow for the use of missed injury rate as a quality indicator for trauma care performance and benchmarking.

Objectives:

This paper reports an evaluation of the properties of a generic electronic health record information model that were actually required and used when importing an existing clinical application into a generic EHR repository.

Method:

A generic EHR repository and system were developed as part of the EU Projects Synapses and SynEx. A Web application to support the management of anticoagulation therapy was developed to interface to the EHR system, and deployed within a north London hospital with five years of cumulative clinical data from the previous existing anticoagulation management application. This offered the opportunity to critique those parts of the generic EHR that were actually needed to represent the legacy data.

Results:

The anticoagulation records from 3,226 patients were imported and represented using over 900,000 Record Components (i.e. each patient’s record contained on average 289 nodes), of which around two thirds were Element Items (i.e. value-containing leaf nodes), the remainder being container nodes (i.e. headings and sub-headings). Each node is capable of incorporating a rich set of context properties, but in reality it was found that many properties were not used at all, and some infrequently (e.g. only around 0.5% of Record Components had ever been revised).

Conclusions:

The process of developing generic EHR information models, arising from research and embodied within new-generation interoperability standards and specifications, has been strongly driven by requirements. These requirements have been gathered primarily by collecting use cases and examples from clinical communities, and been added to successive generations of these models. A priority setting approach has not to date been pursued - all requirements have been received and almost invariably met. This work has shown how little of the resulting model is actually needed to represent useful and usable clinical data. A wider range of such evaluations, looking at different kinds of existing clinical system, is needed to balance the theoretical requirements gathering processes, in order to result in EHR information models of an ideal level of complexity.

Background

Information on ethnicity is commonly used by health services and researchers to plan services, ensure equality of access, and for epidemiological studies. In common with other important demographic and clinical data it is often incompletely recorded. This paper presents a method for imputing missing data on the ethnicity of cancer patients, developed for a regional cancer registry in the UK.

Methods

Routine records from cancer screening services, name recognition software (Nam Pehchan and Onomap), 2001 national Census data, and multiple imputation were used to predict the ethnicity of the 23% of cases that were still missing following linkage with self-reported ethnicity from inpatient hospital records.

Results

The name recognition software were good predictors of ethnicity for South Asian cancer cases when compared with data on ethnicity derived from hospital inpatient records, especially when combined (sensitivity 90.5%; specificity 99.9%; PPV 93.3%). Onomap was a poor predictor of ethnicity for other minority ethnic groups (sensitivity 4.4% for Black cases and 0.0% for Chinese/Other ethnic groups). Area-based data derived from the national Census was also a poor predictor non-White ethnicity (sensitivity: South Asian 7.4%; Black 2.3%; Chinese/Other 0.0%; Mixed 0.0%).

Conclusions

Currently, neither method for assigning individuals to an ethnic group (name recognition and ethnic distribution of area of residence) performs well across all ethnic groups. We recommend further development of name recognition applications and the identification of additional methods for predicting ethnicity to improve their precision and accuracy for comparisons of health outcomes. However, real improvements can only come from better recording of ethnicity by health services.

Researchers have conducted numerous case studies reporting the details on how laboratory test results of patients were missed by the ordering medical providers. Given the importance of timely test results in an outpatient setting, there is limited discussion of electronic versions of test result management tools to help clinicians and medical staff with this complex process. This paper presents three ideas to reduce missed results with a system that facilitates tracking laboratory tests from order to completion as well as during follow-up: (1) define a workflow management model that clarifies responsible agents and associated time frame, (2) generate a user interface for tracking that could eventually be integrated into current electronic health record (EHR) systems, (3) help identify common problems in past orders through retrospective analyses.

Background

Physicians’ cancer-related family history assessment for Lynch syndrome is often inadequate. Furthermore, the extent to which clinicians recognize non-family history-related clues for Lynch syndrome is unclear. We reviewed an integrated electronic health record (EHR) to determine diagnostic evaluation for Lynch syndrome in patients diagnosed with colorectal cancer (CRC).

Methods

We conducted a retrospective cohort study of consecutive patients with CRC, newly diagnosed at a tertiary care VA facility, between 1999 and 2007. A detailed review of the EHR was conducted to evaluate the presence of family-history and non-family history-related criteria of the Bethesda guidelines. Patient outcomes (identification in clinical practice and referral for genetic testing) were also determined.

Results

We identified a total of 499 patients (mean age=65.4 years, 98.6% male, 51.1% non-Hispanic white). At least 1 of the Bethesda criterion was met for 57 patients (11.4%); none were met for 198 (39.7%); and there was uncertainty for 244 (48.9%) because of inadequate family history documentation and/or the patient was unsure about their family history. Forty-nine patients met criteria unrelated to family history. Only 4 of 57 patients (7%) that met the Bethesda guidelines had documentation of counseling. Among 244 patients with uncertainty, a suspicion for Lynch syndrome was documented in the EHR of 6 patients (2.5%); 3 received counseling.

Conclusions

Lynch syndrome is under-recognized, even when patients have clear criteria unrelated to family history. Multifaceted strategies focused on reducing providers’ cognitive errors and harnessing EHR capabilities to improve recognition of Lynch syndrome are needed.

Currently, developers of decision-support systems try to integrate these systems with the electronic medical record. The drawback is a limited amount of recorded medical data. System developers who face the choice between designing an integrated 'non-inquisitive' system and an integrated 'inquisitive' system need insight into the availability of information that is being missed by the support system. Therefore, we have investigated in a simulation study, the reasons why information that was being missed from the electronic medical records of patients with asthma/COPD by reviewers, had not been recorded by general practitioners. Important reasons were: the physicians had not recorded the information explicitly, they assumed the requested information to be common knowledge, and the information was available elsewhere in the electronic medical record. Also, we investigated the reasons why information that was being missed, could not be made available by the physicians. Important reasons were: the decision had been made by another decision maker, or the physician had not recorded the information at the time of the encounter. In addition to insight into the availability of missing information, system developers need to have insight into the significance of this information for the quality of the decision support, before the final choice between a non-inquisitive and an inquisitive design can be made.

Abstract: The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.

As the use of information technology within the healthcare setting increases, the impact of bridging registry data with electronic health records (EHRs) must be addressed. Current EHR implementation may create benefits as well as challenges to cancer registries in areas such as policies and regulations, data quality, reporting, management, staffing, and training. The purpose of this study was to assess 1) the status of EHR usage in cancer registries, 2) the impact of EHR usage on cancer registries, and 3) the benefits and challenges of EHR usage for cancer registries in Alabama. The study method consisted of a voluntary survey provided to participants at the Alabama Cancer Registry Association 2009 annual conference. Forty-three respondents completed the survey. Data indicated that the major benefits of EHR use for the cancer registry included more complete treatment information available to clinicians and researchers, more time for retrieving and analyzing data for clinicians and researchers, and better tracking of patient follow-up. The major challenges included lack of adequate resources, lack of medical staff support, and changing data standards. The conclusion of the study indicates that understanding the impacts and challenges of EHR usage within cancer registries has implications for public health data management, data reporting, and policy issues.

This study exploited the unique opportunity to compare estimates of electronic health record (EHR) and specific health information technology (HIT) use for clinical activities by office-based physicians using data from two contemporaneous, nationally representative physician surveys: the 2008 National Ambulatory Medical Care Survey (NAMCS) and the 2008 Health Tracking Physician Survey (HTPS). Survey respondents included 4,117 physicians from the HTPS and 1,187 physicians from the NAMCS. We compared the survey designs and national estimates of EHR and specific HIT use for clinical activities in the two surveys and conducted multivariate analyses examining physician and practice characteristics associated with the adoption of “basic” or “fully functional” systems. The surveys asked nearly identical questions on EHR use.

Questions on specific HIT use for clinical activities overlapped but with differences. National estimates of all-EHR use were similar (HTPS 24.31 percent, 95 percent confidence interval [CI]: 22.99–25.69 percent vs. NAMCS 27.24 percent, 95 percent CI: 23.53–31.29 percent), but partial EHR use (i.e., part paper and part electronic) was higher in the HTPS than in the NAMCS (23.93 percent, 95 percent CI: 22.61–25.30 percent vs. 18.40 percent, 95 percent CI: 15.62–21.54 percent in the NAMCS). Both surveys reported low use of “fully functional” systems (HTPS 7.84 percent, 95 percent CI: 7.03–8.73 percent vs. NAMCS 4.56 percent, 95 percent CI 3.09–6.68 percent), but the use of “basic” systems was much higher in the HTPS than in the NAMCS (22.29 percent vs. 11.16 percent). Using multivariate analyses, we found common physician or practice characteristics in the two surveys, although the magnitude of the estimated effects differed. In conclusion, use of a “fully functional” EHR system by office-based physicians was low in both surveys. It may be a daunting task for physicians, particularly those in small practices, to adopt and achieve “meaningful use” in the next two years.

Biomedical terminologies are focused on what is general, Electronic Health Records (EHRs) on what is particular, and it is commonly assumed that the step from the one to the other is unproblematic. We argue that this is not so, and that, if the EHR of the future is to fulfill its promise, then the foundations of both EHR architectures and biomedical terminologies need to be reconceived. We accordingly describe a new framework for the treatment of both generals and particulars in biomedical information systems that is designed: 1) to provide new opportunities for the sharing and management of data within and between healthcare institutions, 2) to facilitate interoperability among different terminology and record systems, and thereby 3) to allow new kinds of reasoning with biomedical data.

EHR/CPOE systems improve completeness of medical record and chemotherapy order documentation, as well as user satisfaction with the medical record system.

Purpose:

Computerized physician order entry (CPOE) in electronic health records (EHR) has been recognized as an important tool in optimal health care provision that can reduce errors and improve safety. The objective of this study is to describe documentation completeness and user satisfaction of medical charts before and after implementation of an outpatient oncology EHR/ CPOE system in a hospital-based outpatient cancer center within three treatment sites.

Methods:

This study is a retrospective chart review of 90 patients who received one of the following regimens between 1999 and 2006: FOLFOX, AC, carboplatin + paclitaxel, ABVD, cisplatin + etoposide, R-CHOP, and clinical trials. Documentation completeness scores were assigned to each chart based on the number of documented data points found out of the total data points assessed. EHR/CPOE documentation completeness was compared with completeness of paper charts orders of the same regimens. A user satisfaction survey of the paper chart and EHR/CPOE system was conducted among the physicians, nurses, and pharmacists who worked with both systems.

Results:

The mean percentage of identified data points successfully found in the EHR/CPOE charts was 93% versus 67% in the paper charts (P < .001). Regimen complexity did not alter the number of data points found. The survey response rate was 64%, and the results showed that satisfaction was statistically significant in favor of the EHR/CPOE system.

Conclusion:

Using EHR/CPOE systems improves completeness of medical record and chemotherapy order documentation and improves user satisfaction with the medical record system. EHR/CPOE requires constant vigilance and maintenance to optimize patient safety.

Background

A practical data point for assessing information quality and value in the Electronic Health Record (EHR) is the professional category of the EHR author. We evaluated and compared free form electronic signatures against LOINC note titles in categorizing the profession of EHR authors.

Methods

A random 1000 clinical document sample was selected and divided into 500 document sets for training and testing. The gold standard for provider classification was generated by dual clinician manual review, disagreements resolved by a third reviewer. Text matching algorithms composed of document titles and author electronic signatures for provider classification were developed on the training set.

Results

Overall, detection of professional classification by note titles alone resulted in 76.1% sensitivity and 69.4% specificity. The aggregate of note titles with electronic signatures resulted in 95.7% sensitivity and 98.5% specificity.

Conclusions

Note titles alone provided fair professional classification. Inclusion of author electronic signatures significantly boosted classification performance.

BACKGROUND

Many HIV-infected persons learn about their diagnosis years after initial infection. The extent to which missed opportunities for HIV testing occur in medical evaluations prior to one's HIV diagnosis is not known.

DESIGN

We performed a 10-year retrospective chart review of patients seen at an HIV intake clinic between January 1994 and June 2001 who 1) tested positive for HIV during the 12 months prior to their presentation at the intake clinic and 2) had at least one encounter recorded in the medical record prior to their HIV-positive status. Data collection included demographics, clinical presentation, and whether HIV testing was recommended to the patient or addressed in any way in the clinical note. Prespecified triggers for physicians to recommend HIV testing, such as specific patient characteristics, symptoms, and physical findings, were recorded for each visit. Multivariable logistic regression was used to identify factors associated with missed opportunities for discussion of HIV testing. Generalized estimating equations were used to account for multiple visits per subject.

RESULTS

Among the 221 patients meeting eligibility criteria, all had triggers for HIV testing found in an encounter note. Triggers were found in 50% (1,702/3,424) of these 221 patients’ medical visits. The median number of visits per patient prior to HIV diagnosis to this single institution was 5; 40% of these visits were to either the emergency department or urgent care clinic. HIV was addressed in 27% of visits in which triggers were identified. The multivariable regression model indicated that patients were more likely to have testing addressed in urgent care clinic (39%), sexually transmitted disease clinic (78%), primary care clinics (32%), and during hospitalization (47%), compared to the emergency department (11%), obstetrics/gynecology (9%), and other specialty clinics (10%) (P < .0001). More recent clinical visits (1997–2001) were more likely to have HIV addressed than earlier visits (P < .0001). Women were offered testing less often than men (P = .07).

CONCLUSIONS

Missed opportunities for addressing HIV testing remain unacceptably high when patients seek medical care in the period before their HIV diagnosis. Despite improvement in recent years, variation by site of care remained important. In particular, the emergency department merits consideration for increased resource commitment to facilitate HIV testing. In order to detect HIV infection prior to advanced immunosuppression, clinicians must become more aware of clinical triggers that suggest a patient's increased risk for this infection and lower the threshold at which HIV testing is recommended.

Background

Modern information technology is changing and provides new challenges to health care. The emergence of the Internet and the electronic health record (EHR) has brought new opportunities for patients to play a more active role in his/her care. Although in many countries patients have the right to access their clinical information, access to clinical records electronically is not common. Patient portals consist of provider-tethered applications that allow patients to electronically access health information that are documented and managed by a health care institution. Although patient portals are already being implemented, it is still unclear in which ways these technologies can influence patient care.

Objective

To systematically review the available evidence on the impact of electronic patient portals on patient care.

Methods

A systematic search was conducted using PubMed and other sources to identify controlled experimental or quasi-experimental studies on the impact of patient portals that were published between 1990 and 2011. A total of 1,306 references from all the publication hits were screened, and 13 papers were retrieved for full text analysis.

Results

We identified 5 papers presenting 4 distinct studies. There were no statistically significant changes between intervention and control group in the 2 randomized controlled trials investigating the effect of patient portals on health outcomes. Significant changes in the patient portal group, compared to a control group, could be observed for the following parameters: quicker decrease in office visit rates and slower increase in telephone contacts; increase in number of messages sent; changes of the medication regimen; and better adherence to treatment.

Conclusions

The number of available controlled studies with regard to patient portals is low. Even when patient portals are often discussed as a way to empower patients and improve quality of care, there is insufficient evidence to support this assumption.

Introduction

Electronic health record (EHR) data enhance opportunities for conducting surveillance of diabetes. The objective of this study was to identify the number of people with diabetes from a diabetes DataLink developed as part of the SUPREME-DM (SUrveillance, PREvention, and ManagEment of Diabetes Mellitus) project, a consortium of 11 integrated health systems that use comprehensive EHR data for research.

Methods

We identified all members of 11 health care systems who had any enrollment from January 2005 through December 2009. For these members, we searched inpatient and outpatient diagnosis codes, laboratory test results, and pharmaceutical dispensings from January 2000 through December 2009 to create indicator variables that could potentially identify a person with diabetes. Using this information, we estimated the number of people with diabetes and among them, the number of incident cases, defined as indication of diabetes after at least 2 years of continuous health system enrollment.

Results

The 11 health systems contributed 15,765,529 unique members, of whom 1,085,947 (6.9%) met 1 or more study criteria for diabetes. The nonstandardized proportion meeting study criteria for diabetes ranged from 4.2% to 12.4% across sites. Most members with diabetes (88%) met multiple criteria. Of the members with diabetes, 428,349 (39.4%) were incident cases.

Conclusion

The SUPREME-DM DataLink is a unique resource that provides an opportunity to conduct comparative effectiveness research, epidemiologic surveillance including longitudinal analyses, and population-based care management studies of people with diabetes. It also provides a useful data source for pragmatic clinical trials of prevention or treatment interventions.

This report, based on a workshop jointly sponsored the National Institute of Biomedical Imaging and Biomedical Engineering and the Office of the National Coordinator for Health Information Technology, examines the role and value of images as multimedia data in electronic health records (EHRs). The workshop, attended by a wide range of stakeholders, was motivated in part by the absence of image data from discussions of meaningful use of health information technology. Collectively, the workshop presenters and participants argued that images are not ancillary data and should be central to health information systems to facilitate clinical decisions and higher quality, efficiency, and safety of care. They emphasized that the imaging community has already developed standards that form the basis of interoperability. Despite the apparent value of images, workshop participants also identified challenges and barriers to their implementation within EHRs. Weighing the opportunities and challenges, workshop participants provided their perspectives on possible paths forward toward fully multimedia EHRs.

Background

Prevention of diabetes and coronary heart disease (CHD) is possible but identification of at-risk patients for targeting interventions is a challenge in primary care.

Methods

We analyzed electronic health record (EHR) data for 122,715 patients from 12 primary care practices. We defined patients with risk factor clustering using metabolic syndrome (MetS) characteristics defined by NCEP-ATPIII criteria; if missing, we used surrogate characteristics, and validated this approach by directly measuring risk factors in a subset of 154 patients. For subjects with at least 3 of 5 MetS criteria measured at baseline (2003-2004), we defined 3 categories: No MetS (0 criteria); At-risk-for MetS (1-2 criteria); and MetS (≥ 3 criteria). We examined new diabetes and CHD incidence, and resource utilization over the subsequent 3-year period (2005-2007) using age-sex-adjusted regression models to compare outcomes by MetS category.

Results

After excluding patients with diabetes/CHD at baseline, 78,293 patients were eligible for analysis. EHR-defined MetS had 73% sensitivity and 91% specificity for directly measured MetS. Diabetes incidence was 1.4% in No MetS; 4.0% in At-risk-for MetS; and 11.0% in MetS (p < 0.0001 for trend; adjusted OR MetS vs No MetS = 6.86 [6.06-7.76]); CHD incidence was 3.2%, 5.3%, and 6.4% respectively (p < 0.0001 for trend; adjusted OR = 1.42 [1.25-1.62]). Costs and resource utilization increased across categories (p < 0.0001 for trends). Results were similar analyzing individuals with all five criteria not missing, or defining MetS as ≥ 2 criteria present.

Conclusion

Risk factor clustering in EHR data identifies primary care patients at increased risk for new diabetes, CHD and higher resource utilization.

In order to assess the benefits and limitations of pathology databases to cancer registries, computerised pathology records of malignant neoplasms diagnosed during 1992 were obtained for a defined area of Scotland for which pathology data were not routinely being used for cancer registration. Apparently 'missed' cancer registrations were identified by computerised probability matching with cancer registration records and their eligibility for registration was determined by reference to medical records, or when these were unavailable, by reference to the text of the original pathology report in conjunction with the local Community Health Index (to establish residency at the time of diagnosis). Misclassifications of site or incidence year were not regarded as 'missed' cases. Of 218 apparently 'missed' cancer registrations identified from computerised pathology records, 133 (5.7% of the revised total number of registrations for the study area in 1992) should have been registered. A further 14 cases were already registered but with misclassified site, morphology and/or behaviour codes. Ascertainment of cases by the Scottish Cancer Registration Scheme seems to be high for most sites. Pathology databases represent a useful additional source of cases but the fact that 71 apparently 'missed' cases were found to be ineligible for registration as independent primary malignant neoplasms suggests that unverified computerised pathology data should not be used uncritically nor independently for cancer registration purposes.