Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support

Pharmacy program to improve care for veterans with transient ischaemic attack: a pilot implementation evaluation

Background

Early evaluation and effective communication to manage transient ischaemic attacks (TIA) may lead to a reduction of up to 70% in recurrent events for patients with TIA/minor stroke, along with reduced costs and lengths of hospital stay.

Methods

We conducted a single site pilot evaluation of a clinical pharmacy programme to improve medication management among TIA patients. The programme included a structured protocol, online identification tool, and a templated discharge checklist. Primary effectiveness measures were change in systolic blood pressure (SBP) 90 days post discharge and prescription of high/moderate potency statins. Contextual aspects and clinical perspectives on the implementation process were evaluated through prospective semistructured interviews with key informants.

Results

The analysis included 75 patients in the preimplementation group and 61 in the postimplementation group. The mean SBP at 90 days post discharge was significantly lower in the post implementation period (pre implementation, 133.3 mm Hg (SD 17.8) vs post implementation, 126.8 mm Hg (16.6); p=0.045). The change in SBP from discharge to 90 days post discharge was greater in the postimplementation period (15.8 mm Hg (20.5) vs 24.8 mm Hg (23.2); p=0.029). The prescription of high/moderate potency statins were similar across groups (pre implementation, 66.7% vs post implementation, 77.4%; p=0.229). Front-line clinicians involved in the pilot study reported positively on the acceptability, appropriateness and feasibility of implementing the protocol without additional cost and within current scope of practice.

Conclusions

Implementation of a clinical protocol outlining medication management and provider communication to ensure rapid postdischarge treatment of TIA patients was associated with SBP improvements. The pilot evaluation demonstrates how clinical pharmacists may play a role in treating low frequency, high stakes cerebrovascular events where early treatment and follow-up are critical.

Exploring the COVID-19 Pandemic as a Catalyst for PHR App User Behavior Change in Taiwan: A Development and Usability Study

Background

During the COVID-19 pandemic, personal health records (PHRs) have enabled patients to monitor and manage their medical data without visiting hospitals and, consequently, minimize their infection risk. Taiwan’s National Health Insurance Administration (NHIA) launched the My Health Bank (MHB) service, a national PHR system through which insured individuals to access their cross-hospital medical data. Furthermore, in 2019, the NHIA released the MHB software development kit (SDK), which enables development of mobile apps with which insured individuals can retrieve their MHB data. However, the NHIA MHB service has its limitations, and the participation rate among insured individuals is low.

Objective

We aimed to integrate the MHB SDK with our developed blockchain-enabled PHR mobile app, which enables patients to access, store, and manage their cross-hospital PHR data. We also collected and analyzed the app’s log data to examine patients’ MHB use during the COVID-19 pandemic.

Methods

We integrated our existing blockchain-enabled mobile app with the MHB SDK to enable NHIA MHB data retrieval. The app utilizes blockchain technology to encrypt the downloaded NHIA MHB data. Existing and new indexes can be synchronized between the app and blockchain nodes, and high security can be achieved for PHR management. Finally, we analyzed the app’s access logs to compare patients’ activities during high and low COVID-19 infection periods.

Results

We successfully integrated the MHB SDK into our mobile app, thereby enabling patients to retrieve their cross-hospital medical data, particularly those related to COVID-19 rapid and polymerase chain reaction testing and vaccination information and progress. We retrospectively collected the app’s log data for the period of July 2019 to June 2021. From January 2020, the preliminary results revealed a steady increase in the number of people who applied to create a blockchain account for access to their medical data and the number of app subscribers among patients who visited the outpatient department (OPD) and emergency department (ED). Notably, for patients who visited the OPD and ED, the peak proportions with respect to the use of the app for OPD and ED notes and laboratory test results also increased year by year. The highest proportions were 52.40% for ED notes in June 2021, 88.10% for ED laboratory test reports in May 2021, 34.61% for OPD notes in June 2021, and 41.87% for OPD laboratory test reports in June 2021. These peaks coincided with Taiwan’s local COVID-19 outbreak lasting from May to June 2021.

Conclusions

This study developed a blockchain-enabled mobile app, which can periodically retrieve and integrate PHRs from the NHIA MHB's cross-hospital data and the investigated hospital's self-pay medical data. Analysis of users’ access logs revealed that the COVID-19 pandemic substantially increased individuals’ use of PHRs and their health awareness with respect to COVID-19 prevention.

Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models

BACKGROUND

Electronic health record (EHR) data is commonly used for secondary purposes such as research and clinical decision support. However, reuse of EHR data presents several challenges including but not limited to identifying all diagnoses associated with a patient's clinical encounter. The purpose of this study was to assess the feasibility of developing a schema to identify and subclassify all structured diagnosis codes for a patient encounter.

METHODS

To develop a subclassification schema we used EHR data from an interhospital transport data repository that contained complete hospital encounter level data. Eight discrete data sources containing structured diagnosis codes were identified. Diagnosis codes were normalized using the Unified Medical Language System and additional EHR data were combined with standardized terminologies to create and validate the subcategories. We then employed random forest to assess the usefulness of the new subcategorized diagnoses to predict post-interhospital transfer mortality by building 2 models, one using standard diagnosis codes, and one using the new subcategorized diagnosis codes.

RESULTS

Six subcategories of diagnoses were identified and validated. The subcategories included: primary or admitting diagnoses (10%), past medical, surgical or social history (9%), problem list (20%), comorbidity (24%), discharge diagnoses (6%), and unmapped diagnoses (31%). The subcategorized model outperformed the standard model, achieving a training AUROC of 0.97 versus 0.95 and testing model AUROC of 0.81 versus 0.46.

DISCUSSION

Our work demonstrates that merging structured diagnosis codes with additional EHR data and secondary data sources provides additional information to understand the role of diagnosis throughout a clinical encounter and improves predictive model performance. Further work is necessary to assess if subcategorizing produces benefits in interpreting the results of prognostic models and/or operationalizing the results in clinical decision support applications.

What Oncologists Want: Identifying Challenges and Preferences on Diagnosis Data Entry to Reduce EHR-Induced Burden and Improve Clinical Data Quality

PURPOSE

Accurate recording of diagnosis (DX) data in electronic health records (EHRs) is important for clinical practice and learning health care. Previous studies show statistically stable patterns of data entry in EHRs that contribute to inaccurate DX, likely because of a lack of data entry support. We conducted qualitative research to characterize the preferences of oncological care providers on cancer DX data entry in EHRs during clinical practice.

METHODS

We conducted semistructured interviews and focus groups to uncover common themes on DX data entry preferences and barriers to accurate DX recording. Then, we developed a survey questionnaire sent to a cohort of oncologists to verify the generalizability of our initial findings. We constrained our participants to a single specialty and institution to ensure similar clinical backgrounds and clinical experience with a single EHR system.

RESULTS

A total of 12 neuro-oncologists and thoracic oncologists were involved in the interviews and focus groups. The survey developed from these two initial thrusts was distributed to 19 participants yielding a 94.7% survey response rate. Clinicians reported similar user interface experiences, barriers, and dissatisfaction with current DX entry systems including repetitive entry operations, difficulty in finding specific DX options, time-consuming interactions, and the need for workarounds to maintain efficiency. The survey revealed inefficient DX search interfaces and challenging entry processes as core barriers.

CONCLUSION

Oncologists seem to be divided between specific DX data entry and time efficiency because of current interfaces and feel hindered by the burdensome and repetitive nature of EHR data entry. Oncologists' top concern for adopting data entry support interventions is ensuring that it provides significant time-saving benefits and increasing workflow efficiency. Future interventions should account for time efficiency, beyond ensuring data entry effectiveness.

The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis

Background

As a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site.

Objective

The purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions.

Methods

The MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized.

Results

Compared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit.

Conclusions

The execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe’s fit at new sites.

Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study

Background

At present, electronic health records (EHRs) are the central focus of clinical informatics given their role as the primary source of clinical data. Despite their granularity, the EHR data heavily rely on manual input and are prone to human errors. Many other sources of data exist in the clinical setting, including digital medical devices such as smart infusion pumps. When incorporated with prescribing data from EHRs, smart pump records (SPRs) are capable of shedding light on actions that take place during the medication use process. However, harmoniz-ing the 2 sources is hindered by multiple technical challenges, and the data quality and utility of SPRs have not been fully realized.

Objective

This study aims to evaluate the quality and utility of SPRs incorporated with EHR data in detecting medication administration errors. Our overarching hypothesis is that SPRs would contribute unique information in the med-ication use process, enabling more comprehensive detection of discrepancies and potential errors in medication administration.

Methods

We evaluated the medication use process of 9 high-risk medications for patients admitted to the neonatal inten-sive care unit during a 1-year period. An automated algorithm was developed to align SPRs with their medica-tion orders in the EHRs using patient ID, medication name, and timestamp. The aligned data were manually re-viewed by a clinical research coordinator and 2 pediatric physicians to identify discrepancies in medication ad-ministration. The data quality of SPRs was assessed with the proportion of information that was linked to valid EHR orders. To evaluate their utility, we compared the frequency and severity of discrepancies captured by the SPR and EHR data, respectively. A novel concordance assessment was also developed to understand the detec-tion power and capabilities of SPR and EHR data.

Results

Approximately 70% of the SPRs contained valid patient IDs and medication names, making them feasible for data integration. After combining the 2 sources, the investigative team reviewed 2307 medication orders with 10,575 medication administration records (MARs) and 23,397 SPRs. A total of 321 MAR and 682 SPR dis-crepancies were identified, with vasopressors showing the highest discrepancy rates, followed by narcotics and total parenteral nutrition. Compared with EHR MARs, substantial dosing discrepancies were more commonly detectable using the SPRs. The concordance analysis showed little overlap between MAR and SPR discrepan-cies, with most discrepancies captured by the SPR data.

Conclusions

We integrated smart infusion pump information with EHR data to analyze the most error-prone phases of the medication lifecycle. The findings suggested that SPRs could be a more reliable data source for medication error detection. Ultimately, it is imperative to integrate SPR information with EHR data to fully detect and mitigate medication administration errors in the clinical setting.

Common Data Elements for Acute Coronary Syndrome: Analysis Based on the Unified Medical Language System

BACKGROUND

Standardization in clinical documentation can increase efficiency and can save time and resources.

OBJECTIVE

The objectives of this work are to compare documentation forms for acute coronary syndrome (ACS), check for standardization, and generate a list of the most common data elements using semantic form annotation with the Unified Medical Language System (UMLS).

METHODS

Forms from registries, studies, risk scores, quality assurance, official guidelines, and routine documentation from four hospitals in Germany were semantically annotated using UMLS. This allowed for automatic comparison of concept frequencies and the generation of a list of the most common concepts.

RESULTS

A total of 3710 forms items from 86 sources were semantically annotated using 842 unique UMLS concepts. Half of all medical concept occurrences were covered by 60 unique concepts, which suggests the existence of a core dataset of relevant concepts. Overlap percentages between forms were relatively low, hinting at inconsistent documentation structures and lack of standardization.

CONCLUSIONS

This analysis shows a lack of standardized and semantically enriched documentation for patients with ACS. Efforts made by official institutions like the European Society for Cardiology have not yet been fully implemented. Utilizing a standardized and annotated core dataset of the most important data concepts could make export and automatic reuse of data easier. The generated list of common data elements is an exemplary implementation suggestion of the concepts to use in a standardized approach.