Journal: BMC medical informatics and decision making
BACKGROUND: Implementation and use of electronic health records (EHRs) could lead to potential improvements in quality of care. However, the use of EHRs also introduces unique and often unexpected patient safety risks. Proactive assessment of risks and vulnerabilities can help address potential EHR-related safety hazards before harm occurs; however, current risk assessment methods are underdeveloped. The overall objective of this project is to develop and validate proactive assessment tools to ensure that EHR-enabled clinical work systems are safe and effective. METHODS: This work is conceptually grounded in an 8-dimension model of safe and effective health information technology use. Our first aim is to develop self-assessment guides that can be used by health care institutions to evaluate certain high-risk components of their EHR-enabled clinical work systems. We will solicit input from subject matter experts and relevant stakeholders to develop guides focused on 9 specific risk areas and will subsequently pilot test the guides with individuals representative of likely users. The second aim will be to examine the utility of the self-assessment guides by beta testing the guides at selected facilities and conducting on-site evaluations. Our multidisciplinary team will use a variety of methods to assess the content validity and perceived usefulness of the guides, including interviews, naturalistic observations, and document analysis. The anticipated output of this work will be a series of self-administered EHR safety assessment guides with clear, actionable, checklist-type items. DISCUSSION: Proactive assessment of patient safety risks increases the resiliency of health care organizations to unanticipated hazards of EHR use. The resulting products and lessons learned from the development of the assessment guides are expected to be helpful to organizations that are beginning the EHR selection and implementation process as well as those that have already implemented EHRs. Findings from our project, currently underway, will inform future efforts to validate and implement tools that can be used by health care organizations to improve the safety of EHR-enabled clinical work systems.
The aim of this study was to develop a new data-mining model to predict axillary lymph node (AxLN) metastasis in primary breast cancer. To achieve this, we used a decision tree-based prediction method-the alternating decision tree (ADTree).
BACKGROUND: One possible approach towards avoiding alert overload and alert fatigue in Computerized Physician Order Entry (CPOE) systems is to tailor their drug safety alerts to the context of the clinical situation. Our objective was to identify the perceptions of physicians on the usefulness of clinical context information for prioritizing and presenting drug safety alerts. METHODS: We performed a questionnaire survey, inquiring CPOE-using physicians from four hospitals in four European countries to estimate the usefulness of 20 possible context factors. RESULTS: The 223 participants identified the ‘severity of the effect’ and the ‘clinical status of the patient’ as the most useful context factors. Further important factors are the ‘complexity of the case’ and the ‘risk factors of the patient’. CONCLUSIONS: Our findings confirm the results of a prior, comparable survey inquiring CPOE researchers. Further research should focus on implementing these context factors in CPOE systems and on subsequently evaluating their impact.
BACKGROUND: Hyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques. METHODS: This study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology.This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tamega e Sousa – EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer.Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia. RESULTS: The application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic. CONCLUSIONS: The findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.
BACKGROUND: There are numerous applications for Health Information Systems (HIS) that support specific tasks in the clinical workflow. The Lean method has been used increasingly to optimize clinical workflows, by removing waste and shortening the delivery cycle time. There are a limited number of studies on Lean applications related to HIS. Therefore, we applied the Lean method to evaluate the clinical processes related to HIS, in order to evaluate its efficiency in removing waste and optimizing the process flow. This paper presents the evaluation findings of these clinical processes, with regards to a critical care information system, known as IntelliVue Clinical Information Portfolio (ICIP), and recommends solutions to the problems that were identified during the study. METHODS: We conducted a case study under actual clinical settings, to investigate how the Lean method can be used to improve the clinical process. We used observations, interviews, and document analysis, to achieve our stated goal. We also applied two tools from the Lean methodology, namely the Value Stream Mapping and the A3 problem-solving tools. We used eVSM software to plot the Value Stream Map and A3 reports. RESULTS: We identified a number of problems related to inefficiency and waste in the clinical process, and proposed an improved process model. CONCLUSIONS: The case study findings show that the Value Stream Mapping and the A3 reports can be used as tools to identify waste and integrate the process steps more efficiently. We also proposed a standardized and improved clinical process model and suggested an integrated information system that combines database and software applications to reduce waste and data redundancy.
BACKGROUND: Secondary use of large scale administrative data is increasingly popular in health services and clinical research, where a user-friendly tool for data management is in great demand. MapReduce technology such as Hadoop is a promising tool for this purpose, though its use has been limited by the lack of user-friendly functions for transforming large scale data into wide table format, where each subject is represented by one row, for use in health services and clinical research. Since the original specification of Pig provides very few functions for column field management, we have developed a novel system called GroupFilterFormat to handle the definition of field and data content based on a Pig Latin script. We have also developed, as an open-source project, several user-defined functions to transform the table format using GroupFilterFormat and to deal with processing that considers date conditions. RESULTS: Having prepared dummy discharge summary data for 2.3 million inpatients and medical activity log data for 950 million events, we used the Elastic Compute Cloud environment provided by Amazon Inc. to execute processing speed and scaling benchmarks. In the speed benchmark test, the response time was significantly reduced and a linear relationship was observed between the quantity of data and processing time in both a small and a very large dataset. The scaling benchmark test showed clear scalability. In our system, doubling the number of nodes resulted in a 47% decrease in processing time. CONCLUSIONS: Our newly developed system is widely accessible as an open resource. This system is very simple and easy to use for researchers who are accustomed to using declarative command syntax for commercial statistical software and Structured Query Language. Although our system needs further sophistication to allow more flexibility in scripts and to improve efficiency in data processing, it shows promise in facilitating the application of MapReduce technology to efficient data processing with large scale administrative data in health services and clinical research.
BACKGROUND: A small pre-test study was conducted to ascertain potential harm and anxiety associated with distributing information about possible cancer treatment options at the time of biopsy, prior to knowledge about a definitive cancer diagnosis. Priming men about the availability of multiple options before they have a confirmed diagnosis may be an opportunity to engage patients in more informed decision-making. METHODS: Men with an elevated PSA test or suspicious Digital Rectal Examination (DRE) who were referred to a urology clinic for a biopsy were randomized to receive either the clinic’s usual care (UC) biopsy instruction sheet (n = 11) or a pre-biopsy educational (ED) packet containing the biopsy instruction sheet along with a booklet about the biopsy procedure and a prostate cancer treatment decision aid originally written for newly diagnosed men that described in detail possible treatment options (n = 18). RESULTS: A total of 62% of men who were approached agreed to be randomized, and 83% of the ED group confirmed they used the materials. Anxiety scores were similar for both groups while awaiting the biopsy procedure, with anxiety scores trending lower in the ED group: 41.2 on a prostate-specific anxiety instrument compared to 51.7 in the UC group (p = 0.13). ED participants reported better overall quality of life while awaiting biopsy compared to the UC group (76.4 vs. 48.5, p = 0.01). The small number of men in the ED group who went on to be diagnosed with cancer reported being better informed about the risks and side effects of each option compared to men diagnosed with cancer in the UC group (p = 0.07). In qualitative discussions, men generally reported they found the pre-biopsy materials to be helpful and indicated having information about possible treatment options reduced their anxiety. However, 2 of 18 men reported they did not want to think about treatment options until after they knew their biopsy results. CONCLUSIONS: In this small sample offering pre-biopsy education about potential treatment options was generally well received by patients, appeared to be beneficial to men who went on to be diagnosed, and did not appear to increase anxiety unnecessarily among those who had a negative biopsy.
BACKGROUND: Pain management is a critical but complex issue for the relief of acute pain, particularly for postoperative pain and severe pain in cancer patients. It also plays important roles in promoting quality of care. The introduction of pain management decision support systems (PM-DSS) is considered a potential solution for addressing the complex problems encountered in pain management. This study aims to investigate factors affecting acceptance of PM-DSS from a nurse anesthetist perspective. METHODS: A questionnaire survey was conducted to collect data from nurse anesthetists in a case hospital. A total of 113 questionnaires were distributed, and 101 complete copies were returned, indicating a valid response rate of 89.3 %. Collected data were analyzed by structure equation modeling using the partial least square tool. RESULTS: The results show that perceived information quality (gamma=.451, p<.001), computer self-efficacy (gamma=.315, p<.01), and organizational structure (gamma=.210, p<.05), both significantly impact nurse anesthetists' perceived usefulness of PM-DSS. Information quality (gamma=.267, p<.05) significantly impacts nurse anesthetists' perceptions of PM-DSS ease of use. Furthermore, both perceived ease of use (beta=.436, p<.001, R2=.487) and perceived usefulness (beta=.443, p<.001, R2=.646) significantly affected nurse anesthetists' PM-DSS acceptance (R2=.640). Thus, the critical role of information quality in the development of clinical decision support system is demonstrated. CONCLUSIONS: The findings of this study enable hospital managers to understand the important considerations for nurse anesthetists in accepting PM-DSS, particularly for the issues related to the improvement of information quality, perceived usefulness and perceived ease of use of the system. In addition, the results also provide useful suggestions for designers and implementers of PM-DSS in improving system development.
The objective was to find evidence to substantiate assertions that electronic applications for medication management in ambulatory care (electronic prescribing, clinical decision support (CDSS), electronic health record, and computer generated paper prescriptions), while intended to reduce prescribing errors, can themselves result in errors that might harm patients or increase risks to patient safety.
BACKGROUND: In searches for clinical trials and systematic reviews, it is said that Google Scholar (GS) should never be used in isolation, but in addition to PubMed, Cochrane, and other trusted sources of information. We therefore performed a study to assess the coverage of GS specifically for the studies included in systematic reviews and evaluate if GS was sensitive enough to be used alone for systematic reviews. METHODS: All the original studies included in 29 systematic reviews published in the Cochrane Database Syst Rev or in the JAMA in 2009 were gathered in a gold standard database. GS was searched for all these studies one by one to assess the percentage of studies which could have been identified by searching only GS. RESULTS: All the 738 original studies included in the gold standard database were retrieved in GS (100%). CONCLUSION: The coverage of GS for the studies included in the systematic reviews is 100%. If the authors of the 29 systematic reviews had used only GS, no reference would have been missed. With some improvement in the research options, to increase its precision, GS could become the leading bibliographic database in medicine and could be used alone for systematic reviews.