Clinical information systems: An opportunity to measure value, investigate and innovate from the real world

Assessing the impact of real-time random safety audits through full propensity score matching on reliable data from the clinical information system

BACKGROUND

Evidence-based care processes are not always applied at the bedside in critically ill patients. Numerous studies have assessed the impact of checklists and related strategies on the process of care and patient outcomes. We aimed to evaluate the effects of real-time random safety audits on process-of-care and outcome variables in critical care patients.

METHODS

This prospective study used data from the clinical information system to evaluate the impact of real-time random safety audits targeting 32 safety measures in two intensive care units during a 9-month period. We compared endpoints between patients attended with safety audits and those not attended with safety audits. The primary endpoint was mortality, measured by Cox hazard regression after full propensity-score matching. Secondary endpoints were the impact on adherence to process-of-care measures and on quality indicators.

RESULTS

We included 871 patients; 228 of these were attended in ≥ 1 real-time random safety audits. Safety audits were carried out on 390 patient-days; most improvements in the process of care were observed in safety measures related to mechanical ventilation, renal function and therapies, nutrition, and clinical information system. Although the group of patients attended in safety audits had more severe disease at ICU admission [APACHE II score 21 (16-27) vs. 20 (15-25), p = 0.023]; included a higher proportion of surgical patients [37.3 % vs. 26.4 %, p = 0.003] and a higher proportion of mechanically ventilated patients [72.8 % vs. 40.3 %, p < 0.001]; averaged more days on mechanical ventilation, central venous catheter, and urinary catheter; and had a longer ICU stay [12.5 (5.5-23.3) vs. 2.9 (1.7-5.9), p < 0.001], ICU mortality did not differ significantly between groups (19.3 % vs. 18.8 % in the group without safety rounds). After full propensity-score matching, Cox hazard regression analysis showed real-time random safety audits were associated with a lower risk of mortality throughout the ICU stay (HR 0.31; 95 %CI 0.20-0.47).

CONCLUSIONS

Real-time random safety audits are associated with a reduction in the risk of ICU mortality. Exploiting data from the clinical information system is useful in assessing the impact of them on the care process, quality indicators, and mortality.

Analysis of adherence to an early mobilization protocol in an intensive care unit: Data collected prospectively over a period of three years by the clinical information system

OBJECTIVE

To determinate the adherence and barriers of our early mobilization protocol in patients who had received mechanical ventilation >48h in routine daily practice through clinical information system during all Intensive Care Unit (ICU) stay.

DESIGN

Observational and prospective cohort study.

SETTING

Polyvalent ICU over a three-year period (2017-2019).

PATIENTS

Adult patients on mechanical ventilation >48h who met the inclusion criteria for the early mobilization protocol.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

Demographics, adherence to the protocol and putative hidden adherence, total number of mobilizations, barriers, artificial airway/ventilatory support at each mobilization level and adverse events.

RESULTS

We analyzed 3269 stay-days from 388 patients with median age of 63 (51-72) years, median APACHE II 23 (18-29) and median ICU stay of 10.1 (6.2-16.5) days. Adherence to the protocol was 56.6% (1850 stay-days), but patients were mobilized in only 32.2% (1472) of all stay-days. The putative hidden adherence was 15.6% (509 stay-days) which would increase adherence to 72.2%. The most common reasons for not mobilizing patients were failure to meeting the criteria for clinical stability in 241 (42%) stay-days and unavailability of physiotherapists in 190 (33%) stay-days. Adverse events occurred in only 6 (0.4%) stay-days.

CONCLUSIONS

Data form Clinical Information System showed although adherence was high, patients were mobilized in only one-third of all stay-days. Knowing the specific reason why patient were not mobilized in each stay-day allow to develop concrete decisions to increase the number of mobilizations.

Patient-ventilator asynchronies during mechanical ventilation: current knowledge and research priorities

BACKGROUND

Mechanical ventilation is common in critically ill patients. This life-saving treatment can cause complications and is also associated with long-term sequelae. Patient-ventilator asynchronies are frequent but underdiagnosed, and they have been associated with worse outcomes.

MAIN BODY

Asynchronies occur when ventilator assistance does not match the patient's demand. Ventilatory overassistance or underassistance translates to different types of asynchronies with different effects on patients. Underassistance can result in an excessive load on respiratory muscles, air hunger, or lung injury due to excessive tidal volumes. Overassistance can result in lower patient inspiratory drive and can lead to reverse triggering, which can also worsen lung injury. Identifying the type of asynchrony and its causes is crucial for effective treatment. Mechanical ventilation and asynchronies can affect hemodynamics. An increase in intrathoracic pressure during ventilation modifies ventricular preload and afterload of ventricles, thereby affecting cardiac output and hemodynamic status. Ineffective efforts can decrease intrathoracic pressure, but double cycling can increase it. Thus, asynchronies can lower the predictive accuracy of some hemodynamic parameters of fluid responsiveness. New research is also exploring the psychological effects of asynchronies. Anxiety and depression are common in survivors of critical illness long after discharge. Patients on mechanical ventilation feel anxiety, fear, agony, and insecurity, which can worsen in the presence of asynchronies. Asynchronies have been associated with worse overall prognosis, but the direct causal relation between poor patient-ventilator interaction and worse outcomes has yet to be clearly demonstrated. Critical care patients generate huge volumes of data that are vastly underexploited. New monitoring systems can analyze waveforms together with other inputs, helping us to detect, analyze, and even predict asynchronies. Big data approaches promise to help us understand asynchronies better and improve their diagnosis and management.

CONCLUSIONS

Although our understanding of asynchronies has increased in recent years, many questions remain to be answered. Evolving concepts in asynchronies, lung crosstalk with other organs, and the difficulties of data management make more efforts necessary in this field.

Handover in Intensive Care

Handover is a frequent and complex task that also implies the transfer of the responsibility of the care. The deficiencies in this process are associated with important gaps in clinical safety and also in patient and professional dissatisfaction, as well as increasing health cost. Efforts to standardize this process have increased in recent years, appearing numerous mnemonic tools. Despite this, local are heterogeneous and the level of training in this area is low. The purpose of this review is to highlight the importance of IT while providing a methodological structure that favors effective IT in ICU, reducing the risk associated with this process. Specifically, this document refers to the handover that is established during shift changes or nursing shifts, during the transfer of patients to other diagnostic and therapeutic areas, and to discharge from the ICU. Emergency situations and the potential participation of patients and relatives are also considered. Formulas for measuring quality are finally proposed and potential improvements are mentioned especially in the field of training.

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.

Study of the uses of Information and Communication Technologies by Pain Treatment Unit Physicians

Adequate use of Information and Communication Technologies (ICTs) in health has been shown to save the patient and caregiver time, improve access to the health system, improve diagnosis and control of disease or treatment. All this results in cost savings, and more importantly, they help improve the quality of service and the lives of patients. The purpose of this study is to analyse the differences in the uses of this ICTs between those physicians that belong to Pain Treatment Units (PU) and other physicians that work in pain not linked to these PUs. An online survey, generated by Netquest online survey tool, was sent to both groups of professionals and the data collected was statistical analysed through a logistic regression methodology which is the Logit binomial model. Our results show that those physicians that belong to PUs use ICTs more frequently and consider it more relevant to their clinical practice.