The 30-day readmission rate in neurosurgery-a useful indicator for quality assessment?

BACKGROUND: A shift in how we evaluate healthcare outcomes has driven the introduction of quality indicators as potential parameters to evaluate value-based healthcare delivery. So far, only few studies have been performed evaluating quality indicators in the context of neurosurgery, especially in the European region. The purpose of this study was to evaluate the 30-day readmission rate, identify reasons for readmission regarding the various neurosurgical diagnoses, and discuss the usefulness of this rate as a potential quality indicator. METHODS: During a 6-year period, a total of 8878 hospitalized patients in our neurosurgical department were retrospectively analyzed and included in this study. Reasons for readmission were identified. Patients' diagnoses and baseline characteristics were obtained in order to identify possible risk factors for readmission. RESULTS: The 30-day readmission rate was 2.9%. The most common reason for unplanned readmissions were surgical site infections. The reasons for readmissions varied significantly between the different underlying neurosurgical diseases (p < 0.001). Multivariate logistic regression revealed hydrocephalus (OR, 4) and shorter length of stay during index admission (OR, 0.9) as risk factors for readmission. CONCLUSIONS: We provided an analysis of reasons for readmission for various neurosurgical diseases in a large patient spectrum in Germany. Although readmission rates are easy to track and an attractive tool for quality assessment, the rate alone cannot be seen as an adequate measure for quality in neurosurgery as it lacks a homogenous definition and depends on the underlying health care system. In addition, strategies for risk adjustment are required.

The rise of quality indicators in neurosurgery: 30-day unplanned reoperation rate evaluated in 3760 patients-a single-center experience.

OBJECTIVE: Quality indicators are emerging as tools to evaluate health care outcomes. Few studies have evaluated indicators suitable for neurosurgery so far. Among others, reoperation rate has been suggested as a possible indicator. We aimed to evaluate the reoperation rate in a large neurosurgery adult collective. METHODS: In this exploratory post hoc analysis, we evaluated all patients operated in our service for elective and emergency surgery between January 2014 and May 2016. Planned and unplanned reoperations were filtered and a quantitative analysis, including uni- and multivariate analyses, was performed. RESULTS: A total of 3760 patients were included in this evaluation. From 378 reoperated patients within 30 days (10.1%), 51 underwent planned procedures (1.4%). Three hundred twenty-seven patients (8.7%) represented the analyzed collective of patients having undergone unplanned surgical procedures, causing a total of 409 from 4268 additional procedures (9.6%). Early unplanned 7-day reoperation rate was 4.5% (n = 193), occurring in 4.5% of patients (n = 193). Postoperative hemorrhage (n = 107, 26.2%) and external ventricle drainage-associated infections or dislocation (n = 105, 25.7 %) were the most common indication for unplanned surgery. CONCLUSION: Unplanned re-operation rate of a neurosurgical service can help to internally evaluate health care outcome and improve quality of care. Benchmarking with this indicator however is not recommendable as results can vary distinctly due to the heterogenic patient collective of each institution. We expect unplanned reoperation rates to be higher in large university hospitals and tertiary centers with complex cases, as compared to center with less complex cases treating patients with lower morbidity. In this study, we deliver an authentic portrait of a large neurosurgical center in Germany.

Connecting healthcare and clinical research: Workflow optimizations through seamless integration of EHR, pseudonymization services and EDC systems.

OBJECTIVE: In the last years, several projects promote the secondary use of routine healthcare data based on electronic health record (EHR) data. In multicenter studies, dedicated pseudonymization services are applied for unified pseudonym handling. Healthcare, clinical research and pseudonymization systems are generally disconnected. Hence, the aim of this research work is to integrate these applications and to evaluate the workflow of clinical research. METHODS: We analyzed and identified technical solutions for legislation compliant automatic pseudonym generation and for the integration into EHR as well as electronic data capture (EDC) systems. The Mainzelliste was used as pseudonymization service, which is available as open source solution and compliant with the data privacy concept in Germany. Subject of the integration was the local EHR and an in-house developed EDC system. A time and motion study was conducted to evaluate the effects on the workflow. RESULTS: Integration of EHR, pseudonymization service and EDC systems is technically feasible and leads to a less fragmented usage of all applications. Generated pseudonyms are obtained from the service hosted at a trusted third party and can now be used in the EDC as well as in the EHR system for direct access and re-identification. The evaluation of 90 registration iterations shows that the time for documentation has been significantly reduced in average by 39.6 s (56.3%) from 71 ± 8 s to 31 ± 5 s per registered study patient. CONCLUSIONS: By incorporating EHR, EDC and pseudonymization systems, it is now feasible to support multicenter studies and registers out of an integrated system landscape within a hospital. Optimizing the workflow of patient registration for clinical research allows reduction of double data entry and transcription errors as well as a seamless transition from clinical routine to research data collection.

ODM Data Analysis-A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data.

INTRODUCTION: A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data. METHODS: The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application's performance and functionality. RESULTS: The system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects. DISCUSSION: Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

Leveraging the EHR4CR platform to support patient inclusion in academic studies: challenges and lessons learned.

BACKGROUND: The development of Electronic Health Records (EHRs) in hospitals offers the ability to reuse data from patient care activities for clinical research. EHR4CR is a European public-private partnership aiming to develop a computerized platform that enables the re-use of data collected from EHRs over its network. However, the reproducibility of queries may depend on attributes of the local data. Our objective was 1/ to describe the different steps that were achieved in order to use the EHR4CR platform and 2/ to identify the specific issues that could impact the final performance of the platform. METHODS: We selected three institutional studies covering various medical domains. The studies included a total of 67 inclusion and exclusion criteria and ran in two University Hospitals. We described the steps required to use the EHR4CR platform for a feasibility study. We also defined metrics to assess each of the steps (including criteria complexity, normalization quality, and data completeness of EHRs). RESULTS: We identified 114 distinct medical concepts from a total of 67 eligibility criteria Among the 114 concepts: 23 (20.2%) corresponded to non-structured data (i.e. for which transformation is needed before analysis), 92 (81%) could be mapped to terminologies used in EHR4CR, and 86 (75%) could be mapped to local terminologies. We identified 51 computable criteria following the normalization process. The normalization was considered by experts to be satisfactory or higher for 64.2% (43/67) of the computable criteria. All of the computable criteria could be expressed using the EHR4CR platform. CONCLUSIONS: We identified a set of issues that could affect the future results of the platform: (a) the normalization of free-text criteria, (b) the translation into computer-friendly criteria and (c) issues related to the execution of the query to clinical data warehouses. We developed and evaluated metrics to better describe the platforms and their result. These metrics could be used for future reports of Clinical Trial Recruitment Support Systems assessment studies, and provide experts and readers with tools to insure the quality of constructed dataset.

What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.

Data dictionaries provide structural meta-information about data definitions in health information technology (HIT) systems. In this regard, reusing healthcare data for secondary purposes offers several advantages (e.g. reduce documentation times or increased data quality). Prerequisites for data reuse are its quality, availability and identical meaning of data. In diverse projects, research data warehouses serve as core components between heterogeneous clinical databases and various research applications. Given the complexity (high number of data elements) and dynamics (regular updates) of electronic health record (EHR) data structures, we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard. Metadata of two large hospitals were automatically inserted into two CMDWs containing 16,230 forms and 310,519 data elements. Automatic updates of metadata are possible as well as semantic annotations. A CMDW allows metadata discovery, data quality assessment and similarity analyses. Common data models for distributed research networks can be established based on similarity analyses.

The Portal of Medical Data Models: Where Have We Been and Where Are We Going?

To address current key problems of medical documentation: lack of transparency, overwhelming amount of medical contents to be documented and missing interoperability, the Portal of Medical Data Models (http://medical-data-models.org/) was established in 2012. Constantly evolving, four years later, the portal displays more than 8900 medical data models with more than 250000 items, of which 84 % have been semantically annotated with UMLS codes to support interoperability. Giving an update on new functions and contents of the portal, two additional export formats have been implemented, allowing the reuse of forms such as HL7's framework Fast Health Interoperability Resources (FHIR) Questionnaires, as well as the OpenDataKit format. Future projects include the implementation of an ODMtoOpenClinica converter, as well as supporting the reuse of forms with Apple's ResearchKit and Android's ResearchStack.

Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting.

BACKGROUND: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems. METHODS: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project. RESULTS: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records. CONCLUSIONS: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.

Converting ODM Metadata to FHIR Questionnaire Resources.

Interoperability between systems and data sharing between domains is becoming more and more important. The portal medical-data-models.org offers more than 5.300 UMLS annotated forms in CDISC ODM format in order to support interoperability, but several additional export formats are available. CDISC's ODM and HL7's framework FHIR Questionnaire resource were analyzed, a mapping between elements created and a converter implemented. The developed converter was integrated into the portal with FHIR Questionnaire XML or JSON download options. New FHIR applications can now use this large library of forms.

When the Infection Hits the Wound: Matched Case-Control Study in a Neurosurgical Patient Collective Including Systematic Literature Review and Risk Factors Analysis.

BACKGROUND: To avoid surgical site infections (SSIs) by identifying patients at high risk for infectious complications, a better understanding of relevant risk factors is required. This manuscript describes a matched case-control study of patients undergoing cranial neurosurgery with postoperative surgical site infections and a systematic literature review. METHODS: From January 2012 to March 2015, 70 patients (2.47%) with SSIs (out of 2819 patients) and 185 controls were identified. Statistical analyses were performed using univariate and multivariate models to identify risk factors associated with SSIs. RESULTS: The time of the onset of SSIs ranged from 8 to 854 days after surgery (median: 42 days). American Society of Anesthesiologists score (P = 0.003), surgical drain (P <0.001), number of previous operations (P <0.001), and implantation of foreign material (P <0.001) were significant risk factors for SSIs in multivariate analysis. In a systematic literature review, the authors identified 20 independent risk factors. CONCLUSIONS: This article provides information to ease the prospective assessment of patients at risk of SSI based on preoperative and postoperative risk factors. Lowering the incidence of SSIs will improve the patient outcomes and the overall quality of the healthcare delivered. To our knowledge, this is the first systemic literature review of SSIs in cranial neurosurgery and analysis of own cases in a wide spectrum.

Portal of medical data models: information infrastructure for medical research and healthcare.

INTRODUCTION: Information systems are a key success factor for medical research and healthcare. Currently, most of these systems apply heterogeneous and proprietary data models, which impede data exchange and integrated data analysis for scientific purposes. Due to the complexity of medical terminology, the overall number of medical data models is very high. At present, the vast majority of these models are not available to the scientific community. The objective of the Portal of Medical Data Models (MDM, https://medical-data-models.org) is to foster sharing of medical data models. METHODS: MDM is a registered European information infrastructure. It provides a multilingual platform for exchange and discussion of data models in medicine, both for medical research and healthcare. The system is developed in collaboration with the University Library of Münster to ensure sustainability. A web front-end enables users to search, view, download and discuss data models. Eleven different export formats are available (ODM, PDF, CDA, CSV, MACRO-XML, REDCap, SQL, SPSS, ADL, R, XLSX). MDM contents were analysed with descriptive statistics. RESULTS: MDM contains 4387 current versions of data models (in total 10,963 versions). 2475 of these models belong to oncology trials. The most common keyword (n = 3826) is 'Clinical Trial'; most frequent diseases are breast cancer, leukemia, lung and colorectal neoplasms. Most common languages of data elements are English (n = 328,557) and German (n = 68,738). Semantic annotations (UMLS codes) are available for 108,412 data items, 2453 item groups and 35,361 code list items. Overall 335,087 UMLS codes are assigned with 21,847 unique codes. Few UMLS codes are used several thousand times, but there is a long tail of rarely used codes in the frequency distribution. DISCUSSION: Expected benefits of the MDM portal are improved and accelerated design of medical data models by sharing best practice, more standardised data models with semantic annotation and better information exchange between information systems, in particular Electronic Data Capture (EDC) and Electronic Health Records (EHR) systems. Contents of the MDM portal need to be further expanded to reach broad coverage of all relevant medical domains. Database URL: https://medical-data-models.org.

Standardized mappings--a framework to combine different semantic mappers into a standardized web-API.

BACKGROUND: Automatic coding of medical terms is an important, but highly complicated and laborious task. OBJECTIVES: To compare and evaluate different strategies a framework with a standardized web-interface was created. Two UMLS mapping strategies are compared to demonstrate the interface. METHODS: The framework is a Java Spring application running on a Tomcat application server. It accepts different parameters and returns results in JSON format. To demonstrate the framework, a list of medical data items was mapped by two different methods: similarity search in a large table of terminology codes versus search in a manually curated repository. These mappings were reviewed by a specialist. RESULTS: The evaluation shows that the framework is flexible (due to standardized interfaces like HTTP and JSON), performant and reliable. Accuracy of automatically assigned codes is limited (up to 40%). CONCLUSION: Combining different semantic mappers into a standardized Web-API is feasible. This framework can be easily enhanced due to its modular design.

Efficiency and effectiveness evaluation of an automated multi-country patient count cohort system.

BACKGROUND: With the increase of clinical trial costs during the last decades, the design of feasibility studies has become an essential process to reduce avoidable and costly protocol amendments. This design includes timelines, targeted sites and budget, together with a list of eligibility criteria that potential participants need to match. The present work was designed to assess the value of obtaining potential study participant counts using an automated patient count cohort system for large multi-country and multi-site trials: the Electronic Health Records for Clinical Research (EHR4CR) system. METHODS: The evaluation focuses on the accuracy of the patient counts and the time invested to obtain these using the EHR4CR platform compared to the current questionnaire based process. This evaluation will assess the patient counts from ten clinical trials at two different sites. In order to assess the accuracy of the results, the numbers obtained following the two processes need to be compared to a baseline number, the "alloyed" gold standard, which was produced by a manual check of patient records. RESULTS: The patient counts obtained using the EHR4CR system were in three evaluated trials more accurate than the ones obtained following the current process whereas in six other trials the current process counts were more accurate. In two of the trials both of the processes had counts within the gold standard's confidence interval. In terms of efficiency the EHR4CR protocol feasibility system proved to save approximately seven calendar days in the process of obtaining patient counts compared to the current manual process. CONCLUSIONS: At the current stage, electronic health record data sources need to be enhanced with better structured data so that these can be re-used for research purposes. With this kind of data, systems such as the EHR4CR are able to provide accurate objective patient counts in a more efficient way than the current methods. Additional research using both structured and unstructured data search technology is needed to assess the value of unstructured data and to compare the amount of efforts needed for data preparation.

A European inventory of data elements for patient recruitment.

INTRODUCTION: In the last few years much work has been conducted in creating systems that support clinical trials for example by utilizing electronic health record data. One of these endeavours is the Electronic Health Record for Clinical Research project (EHR4CR). An unanswered question that the project aims to answer is which data elements are most commonly required for patient recruitment. METHODS: Free text eligibility criteria from 40 studies were analysed, simplified and elements were extracted. These elements where then added to an existing inventory of data elements for protocol feasibility. RESULTS: We simplified and extracted data elements from 40 trials, which resulted in 1170 elements. From these we created an inventory of 150 unique data elements relevant for patient identification and recruitment with definitions and referenced codes to standard terminologies. DISCUSSION: Our list was created with expertise from pharmaceutical companies. Comparisons with related work shows that identified concepts are similar. An evaluation of the availability of these elements in electronic health records is still ongoing. Hospitals that want to engage in re-use of electronic health record data for research purposes, for example by joining networks like EHR4CR, can now prioritize their effort based on this list.

Analysis of eligibility criteria from ClinicalTrials.gov.

Electronic health care records are being used more and more for patient documentation. This electronic data can be used for secondary purposes, for example through systems that support clinical research. Eligibility criteria have to be processable for such systems to work, but criteria published on ClinicalTrials.gov have been shown to be complex, making them challenging to re-use. We analysed the eligibility criteria on ClinicalTrials.gov using automatic methods to determine whether the criteria definition and number changed over time. From 1998 to 2012 the average number of words used to describe eligibility criteria per year increased by 46%, while the average number of lines used per year only slightly increases until 2000 and stabilizes afterwards. Whether the increase of words resulted in increased criteria complexity or whether more data elements are used to describe eligibility needs further investigation.

A European inventory of common electronic health record data elements for clinical trial feasibility.

BACKGROUND: Clinical studies are a necessity for new medications and therapies. Many studies, however, struggle to meet their recruitment numbers in time or have problems in meeting them at all. With increasing numbers of electronic health records (EHRs) in hospitals, huge databanks emerge that could be utilized to support research. The Innovative Medicine Initiative (IMI) funded project 'Electronic Health Records for Clinical Research' (EHR4CR) created a standardized and homogenous inventory of data elements to support research by utilizing EHRs. Our aim was to develop a Data Inventory that contains elements required for site feasibility analysis. METHODS: The Data Inventory was created in an iterative, consensus driven approach, by a group of up to 30 people consisting of pharmaceutical experts and informatics specialists. An initial list was subsequently expanded by data elements of simplified eligibility criteria from clinical trial protocols. Each element was manually reviewed by pharmaceutical experts and standard definitions were identified and added. To verify their availability, data exports of the source systems at eleven university hospitals throughout Europe were conducted and evaluated. RESULTS: The Data Inventory consists of 75 data elements that, on the one hand are frequently used in clinical studies, and on the other hand are available in European EHR systems. Rankings of data elements were created from the results of the data exports. In addition a sub-list was created with 21 data elements that were separated from the Data Inventory because of their low usage in routine documentation. CONCLUSION: The data elements in the Data Inventory were identified with the knowledge of domain experts from pharmaceutical companies. Currently, not all information that is frequently used in site feasibility is documented in routine patient care.

Development of best practice principles for simplifying eligibility criteria.

Real world data is crucial for clinical research. In hospitals this information is more and more stored in electronic health records (EHR) and could be used for clinical trial feasibility and patient recruitment. On the other side eligibility criteria within study protocols are complex and usually not written in a way that is intuitively understandable or electronically usable. As part of overcoming the gap between eligibility criteria and the data that is available in EHRs we simplified eligibility criteria and created a guideline with best practice principles. The guideline explains how understandable criteria should be formulated and illustrates with examples what 'good' and 'bad' criteria are.