Exploring the frontier of electronic health record surveillance: the case of postoperative complications.

BACKGROUND: The aim of this study was to build electronic algorithms using a combination of structured data and natural language processing (NLP) of text notes for potential safety surveillance of 9 postoperative complications. METHODS: Postoperative complications from 6 medical centers in the Southeastern United States were obtained from the Veterans Affairs Surgical Quality Improvement Program (VASQIP) registry. Development and test datasets were constructed using stratification by facility and date of procedure for patients with and without complications. Algorithms were developed from VASQIP outcome definitions using NLP-coded concepts, regular expressions, and structured data. The VASQIP nurse reviewer served as the reference standard for evaluating sensitivity and specificity. The algorithms were designed in the development and evaluated in the test dataset. RESULTS: Sensitivity and specificity in the test set were 85% and 92% for acute renal failure, 80% and 93% for sepsis, 56% and 94% for deep vein thrombosis, 80% and 97% for pulmonary embolism, 88% and 89% for acute myocardial infarction, 88% and 92% for cardiac arrest, 80% and 90% for pneumonia, 95% and 80% for urinary tract infection, and 77% and 63% for wound infection, respectively. A third of the complications occurred outside of the hospital setting. CONCLUSIONS: Computer algorithms on data extracted from the electronic health record produced respectable sensitivity and specificity across a large sample of patients seen in 6 different medical centers. This study demonstrates the utility of combining NLP with structured data for mining the information contained within the electronic health record.

Automated identification of postoperative complications within an electronic medical record using natural language processing.

CONTEXT: Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach. OBJECTIVE: To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record. DESIGN, SETTING, AND PATIENTS: Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006. MAIN OUTCOME MEASURES: Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information. RESULTS: The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses. CONCLUSION: Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.

Are posttraumatic stress disorder mental health terms found in SNOMED-CT medical terminology.

The authors sought to evaluate how well the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) controlled vocabulary represents terms commonly used clinically when documenting posttraumatic stress disorder (PTSD). A list was constructed based on the PTSD criteria in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American Psychiatric Association, 1994), symptom assessment instruments, and publications. Although two teams mapping the terms to SNOMED-CT differed in their approach, the consensus mapping accounted for 91% of the 153 PTSD terms. They found that the words used by clinicians in describing PTSD symptoms are represented in SNOMED-CT. These results can be used to codify mental health text reports for health information technology applications such as automated chart abstraction, algorithms for identifying documentation of symptoms representing PTSD in clinical notes, and clinical decision support.

Prevalence and Risk Factors for Opioid-Induced Constipation in an Older National Veteran Cohort.

Objectives: This research describes the prevalence and covariates associated with opioid-induced constipation (OIC) in an observational cohort study utilizing a national veteran cohort and integrated data from the Center for Medicare and Medicaid Services (CMS). Methods: A cohort of 152,904 veterans with encounters between 1 January 2008 and 30 November 2010, an exposure to opioids of 30 days or more, and no exposure in the prior year was developed to establish existing conditions and medications at the start of the opioid exposure and determining outcomes through the end of exposure. OIC was identified through additions/changes in laxative prescriptions, all-cause constipation identification through diagnosis, or constipation related procedures in the presence of opioid exposure. The association of time to constipation with opioid use was analyzed using Cox proportional hazard regression adjusted for patient characteristics, concomitant medications, laboratory tests, and comorbidities. Results: The prevalence of OIC was 12.6%. Twelve positively associated covariates were identified with the largest associations for prior constipation and prevalent laxative (any laxative that continued into the first day of opioid exposure). Among the 17 negatively associated covariates, the largest associations were for erythromycins, androgens/anabolics, and unknown race. Conclusions: There were several novel covariates found that are seen in the all-cause chronic constipation literature but have not been reported for opioid-induced constipation. Some are modifiable covariates, particularly medication coadministration, which may assist clinicians and researchers in risk stratification efforts when initiating opioid medications. The integration of CMS data supports the robustness of the analysis and may be of interest in the elderly population warranting future examination.

No more lost clinic charges: using IT for recovery and measurement.

Identifying missed opportunities for revenue requires examining front-end processes and how they might join with transactions generated during processes of care. Linking the initiation of a charge to an important process in the clinic procedures helps ensure capture of both professional and facility charges. Implementing a software program to automatically initiate correlated charges and measuring the change in charges and revenue are steps that provide a substantial revenue opportunity for healthcare organizations.

Medication administration discrepancies persist despite electronic ordering.

Background Up to 38% of inpatient medication errors occur at the administration stage. Although they reduce prescribing errors, computerized provider order entry (CPOE) systems do not prevent administration errors or timing discrepancies. This study determined the degree to which CPOE medication orders matched actual dose administration times. METHODS At a 658-bed academic hospital with CPOE but lacking electronic medication administration charting, authors randomly selected adult patients with eligible medication orders from historical 1999-2003 CPOE log files. Retrospective manual chart audits compared expected (from CPOE) and actual timing of medication administrations. Outcomes included: dose omissions, median lag times between ordered and charted administrations, unauthorized doses, wrong dose errors, and the rate of nurses' medication schedule shifting. RESULTS Dose omissions occurred in 756 of 6019 (12.6%) audited administration opportunities; only 313 of the omissions (5.2% of opportunities) were unexplained. Wrong doses and unexpected doses occurred for 0.1% and 0.7% of opportunities, respectively. Median lag from expected first dose to actual charted administration time was 27 minutes (IQR 0-127). Nursing staff shifted from ordered to alternate administration schedules for 10.7% of regularly scheduled recurring medication orders. Chart review identified reasons for dose omissions, delays, and dose shifting. CONCLUSION Inpatient CPOE orders are legible and conveyed electronically to nurses and the pharmacy. Nonetheless, ward-based medication administrations do not consistently occur as ordered. Medication administration discrepancies are likely to persist even after implementing CPOE and bar-coded medication administration unless recommended interventions are made to address issues such as determining the true urgency of medication administration, avoiding overlapping duplicative medication orders, and developing a safe means for shifting dosing schedules.

Case report: activity diagrams for integrating electronic prescribing tools into clinical workflow.

To facilitate the future implementation of an electronic prescribing system, this case study modeled prescription management processes in various primary care settings. The Vanderbilt e-prescribing design team conducted initial interviews with clinic managers, physicians and nurses, and then represented the sequences of steps carried out to complete prescriptions in activity diagrams. The diagrams covered outpatient prescribing for patients during a clinic visit and between clinic visits. Practice size, practice setting, and practice specialty type influenced the prescribing processes used. The model developed may be useful to others engaged in building or tailoring an e-prescribing system to meet the specific workflows of various clinic settings.

A randomized placebo-controlled pilot study of the impact of healing touch on fatigue in breast cancer patients undergoing radiation therapy.

PURPOSE: This is a pilot study investigating the effect of healing touch (HT) on fatigue in breast cancer patients undergoing radiation therapy (RT). METHODS/DESIGN: This study presents the results of a within-subjects design randomized clinical trial where the treatment group was treated with HT, whereas the control group experienced sham therapy. The setting was a university RT clinic. The participants were breast cancer patients treated with lumpectomy or mastectomy, 21 to 75 years old with an Eastern Cooperative Oncology Group (ECOG) score of 0 to 2. The intervention was a 45-minute session of HT or sham therapy once a week during RT. Outcome measures included fatigue, quality of life (QOL), and anxiety/depression. RESULT: A total of 70 patients were approached, with 41 completing the study. At completion, the HT participants tended to report higher levels of fatigue, statistically significant for interference ( : = .010) and usual fatigue ( : = .024). The control group tended to report greater reductions in fatigue relative to their own means than the HT group (Cohen's : = 0.30 to 0.49 vs 0.06 to 0.18, respectively). There were no statistically significant differences between the groups for QOL. CONCLUSION: Our enrollment and retention indicate that HT is feasible for women during RT. Our pilot findings do not support a beneficial effect of HT on fatigue or QOL. Future research may explore increasing dose and teasing out therapist effect.

Who said it? Establishing professional attribution among authors of Veterans' Electronic Health Records.

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.

Detection of infectious symptoms from VA emergency department and primary care clinical documentation.

OBJECTIVE: The majority of clinical symptoms are stored as free text in the clinical record, and this information can inform clinical decision support and automated surveillance efforts if it can be accurately processed into computer interpretable data. METHODS: We developed rule-based algorithms and evaluated a natural language processing (NLP) system for infectious symptom detection using clinical narratives. Training (60) and testing (444) documents were randomly selected from VA emergency department, urgent care, and primary care records. Each document was processed with NLP and independently manually reviewed by two clinicians with adjudication by referee. Infectious symptom detection rules were developed in the training set using keywords and SNOMED-CT concepts, and subsequently evaluated using the testing set. RESULTS: Overall symptom detection performance was measured with a precision of 0.91, a recall of 0.84, and an F measure of 0.87. Overall symptom detection with assertion performance was measured with a precision of 0.67, a recall of 0.62, and an F measure of 0.64. Among those instances in which the automated system matched the reference set determination for symptom, the system correctly detected 84.7% of positive assertions, 75.1% of negative assertions, and 0.7% of uncertain assertions. CONCLUSION: This work demonstrates how processed text could enable detection of non-specific symptom clusters for use in automated surveillance activities.

Medication-error alerts for warfarin orders detected by a bar-code-assisted medication administration system.

PURPOSE: Medication-error alerts for warfarin orders detected by a bar-code-assisted medication administration (BCMA) system were evaluated. METHODS: All patients receiving warfarin who were admitted to a university medical center between July 1, 2008, and February 6, 2009, in inpatient units with BCMA systems were candidates for inclusion in this study. Medication-error alerts displayed to the nurse administering the warfarin were reviewed to determine whether a true potential error was detected. Each alert was converted to a scenario, and its potential to require treatment or cause patient harm was rated using a validated severity scale of 0-10, where a score of 0 indicated no probable effect on the patient and 10 indicated that the error would likely result in patient death. A severity score was obtained by averaging the scores of four pharmacist reviewers. RESULTS: Of the 18,393 warfarin doses ordered during the study period for 2,404 patients, error alerts associated with only 99 warfarin doses were found to be clinically meaningful. The mean ± S.D. severity rating of these alerts was low (2.93 ± 1.42), with a standardized Cronbach's coefficient alpha of 0.845. The mean ± S.D. warfarin dose attempted when the nurse received an alert was 4.10 ± 2.48 mg. The majority of doses with alerts (70%) were for patients who had an active order for warfarin. CONCLUSION: Of the large number of medication-error alerts generated through a BCMA system, only a small proportion were considered clinically significant. This indicated that the rate of false-positive alerts was unexpectedly high, increasing the risk of alert fatigue.

Detection of blood culture bacterial contamination using natural language processing.

Microbiology results are reported in semi-structured formats and have a high content of useful patient information. We developed and validated a hybrid regular expression and natural language processing solution for processing blood culture microbiology reports. Multi-center Veterans Affairs training and testing data sets were randomly extracted and manually reviewed to determine the culture and sensitivity as well as contamination results. The tool was iteratively developed for both outcomes using a training dataset, and then evaluated on the test dataset to determine antibiotic susceptibility data extraction and contamination detection performance. Our algorithm had a sensitivity of 84.8% and a positive predictive value of 96.0% for mapping the antibiotics and bacteria with appropriate sensitivity findings in the test data. The bacterial contamination detection algorithm had a sensitivity of 83.3% and a positive predictive value of 81.8%.

Return on investment point of service Computerized Provider Charge Entry.

Provider charge entry systems offer many benefits to users and organizations. At Vanderbilt University Medical Center, a web-based provider charge entry system promises to deliver benefits in reducing days in accounts receivable, reducing labor required for claims and edit processing, and implementing business rules that deliver both strategic and financial benefits.

Implementing outpatient order entry to support medical necessity using the patient's electronic past medical history.

Physician order entry is difficult to implement, both in inpatient and outpatient settings. Such systems must integrate conveniently into clinical workflows, and provide sufficient benefit to offset the burden of system use. For outpatient order entry, significant advantages can accrue when systems incorporate medical necessity guidelines - improved billing and adherence to governmental policies. The authors developed and implemented an outpatient order entry system that utilizes an electronically accessible history of patient, provider, and clinic-related diagnoses in assisting providers (when possible and appropriate) to select compliant justifications for tests and procedures. The pilot implementation site, active for more than six months, has been the Vanderbilt University Page Campbell Cardiology Clinic, with 34 providers.

Quill: a novel approach to structured reporting.

The authors will demonstrate Quill (QUestions and Information Logically Linked), a comprehensive structured reporting environment for ambulatory care that was developed at the Vanderbilt University Medical Center. A notes capture tool was sought with the immediate hope of decreasing or eliminating transcription costs (currently around $6M/yr) and paper based processing while providing a foundation for decision support and research in the future.