Automatic quality improvement reports in the intensive care unit: One step closer toward meaningful use

Secondary use of data extracted from a clinical information system to assess the adherence of tidal volume and its impact on outcomes

OBJECTIVES

To extract data from clinical information systems to automatically calculate high-resolution quality indicators to assess adherence to recommendations for low tidal volume.

DESIGN

We devised two indicators: the percentage of time under mechanical ventilation with excessive tidal volume (>8mL/kg predicted body weight) and the percentage of patients who received appropriate tidal volume (≤8mL/kg PBW) at least 80% of the time under mechanical ventilation. We developed an algorithm to automatically calculate these indicators from clinical information system data and analyzed associations between them and patients' characteristics and outcomes.

SETTINGS

This study has been carried out in our 30-bed polyvalent intensive care unit between January 1, 2014 and November 30, 2019.

PATIENTS

All patients admitted to intensive care unit ventilated >72h were included.

INTERVENTION

Use data collected automatically from the clinical information systems to assess adherence to tidal volume recommendations and its outcomes.

MAIN VARIABLES OF INTEREST

Mechanical ventilation days, ICU length of stay and mortality.

RESULTS

Of all admitted patients, 340 met the inclusion criteria. Median percentage of time under mechanical ventilation with excessive tidal volume was 70% (23%-93%); only 22.3% of patients received appropriate tidal volume at least 80% of the time. Receiving appropriate tidal volume was associated with shorter duration of mechanical ventilation and intensive care unit stay. Patients receiving appropriate tidal volume were mostly male, younger, taller, and less severely ill. Adjusted intensive care unit mortality did not differ according to percentage of time with excessive tidal volume or to receiving appropriate tidal volume at least 80% of the time.

CONCLUSIONS

Automatic calculation of process-of-care indicators from clinical information systems high-resolution data can provide an accurate and continuous measure of adherence to recommendations. Adherence to tidal volume recommendations was associated with shorter duration of mechanical ventilation and intensive care unit stay.

Automatic generation of minimum dataset and quality indicators from data collected routinely by the clinical information system in an intensive care unit

BACKGROUND

Quality indicators (QIs) are being increasingly used in medicine to compare and improve the quality of care delivered. The feasibility of data collection is an important prerequisite for QIs. Information technology can improve efforts to measure processes and outcomes. In intensive care units (ICU), QIs can be automatically measured by exploiting data from clinical information systems (CIS).

OBJECTIVE

To describe the development and application of a tool to automatically generate a minimum dataset (MDS) and a set of ICU quality metrics from CIS data.

METHODS

We used the definitions for MDS and QIs proposed by the Spanish Society of Critical Care Medicine and Coronary Units. Our tool uses an extraction, transform, and load process implemented with Python to extract data stored in various tables in the CIS database and create a new associative database. This new database is uploaded to Qlik Sense, which constructs the MDS and calculates the QIs by applying the required metrics. The tool was tested using data from patients attended in a 30-bed polyvalent ICU during a six-year period.

RESULTS

We describe the definitions and metrics, and we report the MDS and QI measurements obtained through the analysis of 4546 admissions. The results show that our ICU's performance on the QIs analyzed meets the standards proposed by our national scientific society.

CONCLUSIONS

This is the first step toward using a tool to automatically obtain a set of actionable QIs to monitor and improve the quality of care in ICUs, eliminating the need for professionals to enter data manually, thus saving time and ensuring data quality.

Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment

BACKGROUND

Big data analytics promise insights into healthcare processes and management, improving outcomes while reducing costs. However, data quality is a major challenge for reliable results. Business process discovery techniques and an associated data model were used to develop data management tool, ICU-DaMa, for extracting variables essential for overseeing the quality of care in the intensive care unit (ICU).

OBJECTIVE

To determine the feasibility of using ICU-DaMa to automatically extract variables for the minimum dataset and ICU quality indicators from the clinical information system (CIS).

METHODS

The Wilcoxon signed-rank test and Fisher's exact test were used to compare the values extracted from the CIS with ICU-DaMa for 25 variables from all patients attended in a polyvalent ICU during a two-month period against the gold standard of values manually extracted by two trained physicians. Discrepancies with the gold standard were classified into plausibility, conformance, and completeness errors.

RESULTS

Data from 149 patients were included. Although there were no significant differences between the automatic method and the manual method, we detected differences in values for five variables, including one plausibility error and two conformance and completeness errors. Plausibility: 1) Sex, ICU-DaMa incorrectly classified one male patient as female (error generated by the Hospital's Admissions Department). Conformance: 2) Reason for isolation, ICU-DaMa failed to detect a human error in which a professional misclassified a patient's isolation. 3) Brain death, ICU-DaMa failed to detect another human error in which a professional likely entered two mutually exclusive values related to the death of the patient (brain death and controlled donation after circulatory death). Completeness: 4) Destination at ICU discharge, ICU-DaMa incorrectly classified two patients due to a professional failing to fill out the patient discharge form when thepatients died. 5) Length of continuous renal replacement therapy, data were missing for one patient because the CRRT device was not connected to the CIS.

CONCLUSIONS

Automatic generation of minimum dataset and ICU quality indicators using ICU-DaMa is feasible. The discrepancies were identified and can be corrected by improving CIS ergonomics, training healthcare professionals in the culture of the quality of information, and using tools for detecting and correcting data errors.

Can a Novel ICU Data Display Positively Affect Patient Outcomes and Save Lives?

The aim of this study was to quantify the impact of ProCCESs AWARE, Ambient Clinical Analytics, Rochester, MN, a novel acute care electronic medical record interface, on a range of care process and patient health outcome metrics in intensive care units (ICUs). ProCCESs AWARE is a novel acute care EMR interface that contains built-in tools for error prevention, practice surveillance, decision support and reporting. We compared outcomes before and after AWARE implementation using a prospective cohort and a historical control. The study population included all critically ill adult patients (over 18 years old) admitted to four ICUs at Mayo Clinic, Rochester, MN, who stayed in hospital at least 24 h. The pre-AWARE cohort included 983 patients from 2010, and the post-AWARE cohort included 856 patients from 2014. We analyzed patient health outcomes, care process quality, and hospital charges. After adjusting for patient acuity and baseline demographics, overall in-hospital and ICU mortality odds ratios associated with AWARE intervention were 0.45 (95% confidence interval 0.30 to 0.70) and 0.38 (0.22, 0.66). ICU length of stay decreased by about 50%, hospital length of stay by 37%, and total charges for hospital stay by 30% in post AWARE cohort (by $43,745 after adjusting for patient acuity and demographics). Better organization of information in the ICU with systems like AWARE has the potential to improve important patient outcomes, such as mortality and length of stay, resulting in reductions in costs of care.

A new era of quality measurement in rheumatology: electronic clinical quality measures and national registries

PURPOSE OF REVIEW

This article reviews the evolution of quality measurement in rheumatology, highlighting new health-information technology infrastructure and standards that are enabling unprecedented innovation in this field.

RECENT FINDINGS

Spurred by landmark legislation that ties physician payment to value, the widespread use of electronic health records, and standards such as the Quality Data Model, quality measurement in rheumatology is rapidly evolving. Rather than relying on retrospective assessments of care gathered through administrative claims or manual chart abstraction, new electronic clinical quality measures (eCQMs) allow automated data capture from electronic health records. At the same time, qualified clinical data registries, like the American College of Rheumatology's Rheumatology Informatics System for Effectiveness registry, are enabling large-scale implementation of eCQMs across national electronic health record networks with real-time performance feedback to clinicians. Although successful examples of eCQM development and implementation in rheumatology and other fields exist, there also remain challenges, such as lack of health system data interoperability and problems with measure accuracy.

SUMMARY

Quality measurement and improvement is increasingly an essential component of rheumatology practice. Advances in health information technology are likely to continue to make implementation of eCQMs easier and measurement more clinically meaningful and accurate in coming years.