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.
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.
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.
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.
Differences in the prevalence of undiagnosed HIV between different types of emergency departments (EDs) are not well understood. We seek to define missed opportunities for HIV diagnosis within 3 geographically proximate EDs serving different patient populations in a single metropolitan area.
For an urban academic, an urban community, and a suburban community ED located within 10 miles of one another, we reviewed visit records for a cohort of patients who received a new diagnosis of HIV between July 1999 and June 2003. Missed opportunities for earlier HIV diagnosis were defined as ED visits in the year before diagnosis, during which there was no documented ED HIV testing offer or test. Outcomes were the number of missed opportunity visits and the number of patients with a missed opportunity for each ED. We secondarily reviewed medical records for missed opportunity encounters, using an extensive list of indications that might conceivably trigger testing.
Among 276 patients with a new HIV diagnosis, 123 (44.5%) visited an ED in the year before diagnosis or received a diagnosis in the ED. The urban academic ED HIV testing program diagnosed 23 (8.3%) cases and offered testing to 24 (8.7%) patients who declined. Missed opportunities occurred during 187 visits made by 76 (27.5%) patients. These included 70 patients with 157 visits at the urban academic ED, 9 patients with 24 visits at the urban community ED, and 4 patients with 6 visits at the suburban community ED. Medical records were available for 172 of the 187 missed opportunity visits. Visits were characterized by the following potential testing indicators: HIV risk factors (58; 34%), related diagnosis indicating risk (7; 4%), AIDS-defining illness (8; 5%), physician suspicion of HIV (29; 17%), and nonspecific signs or symptoms of illness potentially consistent with HIV (126; 73%).
Geographically proximate EDs differ in their opportunities for earlier HIV diagnosis, but all 3 sites had missed opportunities. Many ED patients with undiagnosed HIV have potential indications for testing documented even in the absence of a dedicated risk assessment, although most of these are nonspecific signs or symptoms of illness that may not be clinically useful selection criteria.
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.
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).
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.
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 Records (EHR) are widely believed to improve quality of care and effectiveness of service delivery. Use of EHR to improve childhood immunization rates has not been fully explored in an ambulatory setting.
To describe a pediatric practice’s use of Electronic Health Records (EHR) in improving childhood immunization.
A multi-faceted EHR-based quality improvement initiative used electronic templates with pre-loaded immunization records, automatic diagnosis coding, and EHR alerts of missing or delayed vaccinations. An electronic patient tracking system was created to identify patients with missing vaccines. Barcode scanning technology was introduced to aid speed and accuracy of documentation of administered vaccines. Electronic reporting to a local health department immunization registry facilitated ordering of vaccines.
Immunization completion rates captured in monthly patient reports showed a rise in the percentage of children receiving the recommended series of vaccination (65% to 76%) (p<0.000). Barcode technology reduced the time of immunization documentation (86 seconds to 26 seconds) (p<0.000). Use of barcode scanning showed increased accuracy of documentation of vaccine lot numbers (from 95% to 100%) (p<0.000).
EHR-based quality improvement interventions were successfully implemented at a community health center. EHR systems have versatility in their ability to track patients in need of vaccines, identify patients who are delayed, facilitate ordering and coding of multiple vaccines and promote interdisciplinary communication among personnel involved in the vaccination process. EHR systems can be used to improve childhood vaccination rates.
Electronic Health Records; pediatrics; childhood vaccinations; immunization registry
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.
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).
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.
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%
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
Predictors of antiretroviral treatment (ART) failure are not well characterized for heterogeneous clinic populations.
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.
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).
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.
adherence; antiretroviral therapy; electronic health record; HIV; treatment failure; virologic failure
Diagnostic errors in primary care are harmful but poorly studied. To facilitate understanding of diagnostic errors in real-world primary care settings using electronic health records (EHRs), this study explored the use of the Situational Awareness (SA) framework from aviation human factors research.
A mixed-methods study was conducted involving reviews of EHR data followed by semi-structured interviews of selected providers from two institutions in the US. The study population included 380 consecutive patients with colorectal and lung cancers diagnosed between February 2008 and January 2009. Using a pre-tested data collection instrument, trained physicians identified diagnostic errors, defined as lack of timely action on one or more established indications for diagnostic work-up for lung and colorectal cancers. Twenty-six providers involved in cases with and without errors were interviewed. Interviews probed for providers' lack of SA and how this may have influenced the diagnostic process.
Of 254 cases meeting inclusion criteria, errors were found in 30 (32.6%) of 92 lung cancer cases and 56 (33.5%) of 167 colorectal cancer cases. Analysis of interviews related to error cases revealed evidence of lack of one of four levels of SA applicable to primary care practice: information perception, information comprehension, forecasting future events, and choosing appropriate action based on the first three levels. In cases without error, the application of the SA framework provided insight into processes involved in attention management.
A framework of SA can help analyze and understand diagnostic errors in primary care settings that use EHRs.
diagnostic error; decision-making; patient safety; primary care; medical errors; human factors; cancer; electronic health records; diagnostic delays
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?
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.
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.
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.
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.
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).
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.
Tertiary survey; Missed injury; Multiple trauma; Patient safety; Quality of care
A primary goal for the development of EHRs and EHR-related technologies should be to facilitate greater knowledge management for improving individual and community health outcomes associated with HIV / AIDS. Most of the current developments of EHR have focused on providing data for research, patient care and prioritization of healthcare provider resources in other areas. More attention should be paid to using information from EHRs to assist local, state, national, and international entities engaged in HIV / AIDS care, research and prevention strategies. Unfortunately the technology and standards for HIV-specific reporting modules are still being developed.
A literature search and review supplemented by the author’s own experiences with electronic health records and HIV / AIDS prevention strategies will be used. This data was used to identify both opportunities and challenges for improving public health informatics primarily through the use of latest innovations in EHRs. Qualitative analysis and suggestions are offered for how EHRs can support knowledge management and prevention strategies associated with HIV infection.
EHR information, including demographics, medical history, medication and allergies, immunization status, and other vital statistics can help public health practitioners to more quickly identify at-risk populations or environments; allocate scarce resources in the most efficient way; share information about successful, evidenced-based prevention strategies; and increase longevity and quality of life.
Local, state, and federal entities need to work more collaboratively with NGOs, community-based organizations, and the private sector to eliminate barriers to implementation including cost, interoperability, accessibility, and information security.
Usability of Health Information; Information Technology; Health Promotion / Disease Prevention; HIV; Acquired Immunodeficiency Syndrome; Public Health Informatics; Electronic Health Records; Knowledge Management
Hospitalizations that occur shortly after emergency department (ED) discharge may reveal opportunities to improve ED or follow-up care. There currently is limited, population-level information about such events. We identified hospital and visit-level predictors of bounce-back admissions, defined as 7-day unscheduled hospital admissions after ED discharge.
Using the California Office of Statewide Health Planning and Development (OSHPD) files, we conducted a retrospective cohort analysis of adult (age≥18 years) ED visits resulting in discharge in 2007. Candidate predictors included index hospital structural characteristics such as ownership, teaching affiliation, trauma status, and index ED size; along with index visit patient characteristics of demographic information, day of service, against medical advice or eloped disposition, insurance, and ED primary discharge diagnosis. We fit a multivariable, hierarchical logistic regression to account for clustering of ED visits by hospitals.
The study cohort contained a total of 5,035,833 visits to 288 facilities in 2007. Bounce-back admission within 7 days occurred in 130,526 (2.6%) visits and was associated with Medicaid (OR 1.42, 95% CI 1.40–1.45) or Medicare insurance (OR 1.53, 95% CI1.50–1.55) and a disposition of leaving against medical advice (AMA) or before the evaluation was complete (OR 1.9, 95% CI 1.89–2.0). The three most common age-adjusted index ED discharge diagnoses associated with a bounce-back admission were chronic renal disease, not end stage (OR 3.3, 95% CI 2.8–3.8), end stage renal disease (OR 2.9, 95% CI 2.4–3.6), and congestive heart failure (OR 2.5, 95% CI 2.3–2.6). Hospital characteristics associated with a higher bounce–back admission rate were for-profit status (OR 1.2, 95% CI 1.1–1.3) and teaching affiliation (OR 1.2, 95% CI 1.0–1.3).
We found 2.6% of discharged patients from California EDs to have a bounce-back admission within 7 days. We identified vulnerable populations, such as the very old and the use of Medicaid Insurance, and chronic or end stage renal disease as being especially at risk. Our findings suggest that quality improvement efforts focus on high-risk individuals and that the disposition plan of patients consider vulnerable populations.
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.
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.
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).
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.
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.
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.
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%).
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.
To estimate the impact of missed opportunities on influenza vaccination coverage among 6 through 23 month old children who sought medical care during the 2004–2005 influenza season.
Retrospective cohort study
Fifty two primary care practice sites located in Rochester New York, Nashville Tennessee and Cincinnati Ohio
Children 6 through 23 months of age
Charts were reviewed and data collected on influenza vaccinations, type of health care visit (well-child or other), and presence of illness symptoms. Missed opportunity was defined as a practice visit by an eligible child during influenza season, when vaccine was available, but during which the child did not receive an influenza vaccination. Vaccine was assumed to be available between the first and last dates influenza vaccination was recorded at that practice. Each child was classified as fully vaccinated, partially vaccinated or unvaccinated.
Data were analyzed for 1724 children 6 through 23 months. Most children (62.0%) had at least one missed opportunity during this period. Among children with any missed opportunities, 12.8% were fully and 29.8% were partially vaccinated. Overall, 33.6% of missed opportunities occurred during well child visits and 66.4% during other types of visits; 75% occurred when no other vaccines were given. Eliminating all missed opportunities would have increased full vaccination coverage from 30.3% to 49.9%.
Missed opportunities for influenza vaccination are frequent. Reducing missed opportunities could significantly increase influenza vaccination rates and should be a goal in each practice.
vaccination; child health services; influenza; human
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.
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).
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.
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.
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.
Lynch syndrome; health outcomes; familial colorectal cancer; practice patterns; missed diagnosis; guideline non-adherence; genetic evaluation; delayed cancer diagnosis
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.
Predictors of long-term survival for patients with lung cancer assist in individualizing treatment recommendations. Diffusing capacity (DLCO) is a predictor of complications after resection for lung cancer. We sought to determine whether DLCO is also prognostic for long-term survival after lung resection for cancer.
We assessed survival among patients in our prospective database who underwent lung resection for cancer between 1980–2006. Potential prognostic factors for all-cause mortality were evaluated by computing average annual hazard rates, and variables significantly associated with survival were included in multivariable Cox modelling. Multiple imputation was used to address missing values.
Among 854 unique patients, there were 587 deaths. The median follow-up time from surgery was 9.6 years. Predictors of survival included age, stage, performance status, body mass index, history of myocardial infarction, renal function and DLCO. On univariate analysis, the hazard ratio increased incrementally compared with those with a DLCO of ≥80% (70–79%, 1.12; 60–69%, 1.29; <60%, 1.35). On multivariable analysis, DLCO was an independent predictor of long-term survival for all patients (corrected for all other important covariates; HR 1.04 per 10-point decrement; 95% CI 1.00–1.08; P = 0.05). Its prognostic ability for long-term survival was above and beyond its influence on operative mortality.
DLCO is an independent and clinically important determinant of long-term survival after major lung resection for cancer, a finding that is not generally known. Knowledge of this may help improve selection of patients for lung resection and may help tailor the extent of resection, when possible, in order to appropriately balance operative risk with long-term outcomes.
Lung resection; Survival; Diffusing capacity; Lung cancer
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.
electronic health records; cancer registry; information technology
Electronic health records (EHRs) have potential quality and safety benefits. However, reports of EHR-related safety hazards are now emerging. The Office of the National Coordinator (ONC) for Health Information Technology (HIT) recently sponsored an Institute of Medicine committee to evaluate how HIT use affects patient safety. In this paper, we propose the creation of a national EHR oversight program to provide dedicated surveillance of EHR-related safety hazards and to promote learning from identified errors, close calls, and adverse events. The program calls for data gathering, investigation/analysis and regulatory components. The first two functions will depend on institution-level EHR safety committees that will investigate all known EHR-related adverse events and near-misses and report them nationally using standardized methods. These committees should also perform routine safety self-assessments to proactively identify new risks. Nationally, we propose the long-term creation of a centralized, non-partisan board with an appropriate legal and regulatory infrastructure to ensure the safety of EHRs. We discuss the rationale of the proposed oversight program and its potential organizational components and functions. These include mechanisms for robust data collection and analyses of all safety concerns using multiple methods that extend beyond reporting; multidisciplinary investigation of selected high-risk safety events; and enhanced coordination with other national agencies in order to facilitate broad dissemination of hazards information. Implementation of this proposed infrastructure can facilitate identification of EHR-related adverse events and errors and potentially create a safer and more effective EHR-based health care delivery system.
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.
health information technology; electronic health records; physician survey
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.
terminologies; Relation Ontology; referent tracking; diagnostic decision support; SNOMED