Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Different expression ratio of S100A8/A9 and S100A12 in acute and chronic lung diseases.

Calgranulins are a family of powerful chemoattractants, which have been implicated as biomarkers in inflammatory diseases. To determine how different respiratory diseases affect the expression of calgranulins, we measured the expression of S100A8/A9 and S100A12 in bronchoalveolar lavage fluid (BALF) of acute respiratory distress syndrome (ARDS) patients and healthy volunteers by ELISA. Analysis of calgranulin expression revealed a high level of S100A12 in the lavages of patients suffering from ARDS compared to controls (p<0.001). Based on the hypothesis that the increased expression of S100A12 relative to the S100A8/A9 heterodimer was a characteristic of respiratory diseases with neutrophilic inflammation, we measured calgranulin expression in BALF of cystic fibrosis (CF) patients. Despite similarly elevated levels of S100A8/A9, S100A12 was significantly higher in ARDS compared to CF BALF (p<0.001). The differential expression of calgranulins was unique for inflammatory markers, as an array of cytokines did not differ between CF and ARDS patients. Since ARDS is an acute event and CF a chronic inflammation with acute exacerbations, we compared calgranulin expression in sputum obtained from CF and patients with chronic obstructive lung disease (COPD). Levels of S100A12 and S100A8/9 were elevated in CF sputum compared to COPD sputum, but the ratio of S100A12 to S100A8/A9 was similar in COPD and CF and reflected more closely than seen in healthy controls. The results indicate that the regulation of human calgranulin expression and the ratio of S100A8/A9 to S100A12 may provide important insights in the mechanism of respiratory inflammation.

Multicenter validation of a computer-based clinical decision support tool for glucose control in adult and pediatric intensive care units.

INTRODUCTION: Hyperglycemia during critical illness is common, and intravenous insulin therapy (IIT) to normalize blood glucose improves outcomes in selected populations. Methods differ widely in complexity, insulin dosing approaches, efficacy, and rates of hypoglycemia. We developed a simple bedside-computerized decision support protocol (eProtocol-insulin) that yields promising results in the development center. We examined the effectiveness and safety of this tool in six adult and five pediatric intensive care units (ICUs) in other centers. METHODS: We required attending physicians of eligible patients to independently intend to use intravenous insulin to normalize blood glucose. We used eProtocol-insulin for glucose control for a duration determined by the clinical caregivers. Adults had an anticipated length of stay of 3 or more days. In pediatric ICUs, we also required support or intended support with mechanical ventilation for greater than 24 hours or with a vasoactive infusion. We recorded all instances in which eProtocol-insulin instructions were not accepted and all blood glucose values. An independent data safety and monitoring board monitored study results and subject safety. Bedside nurses were selected randomly to complete a paper survey describing their perceptions of quality of care and workload related to eProtocol-insulin use. RESULTS: Clinicians accepted 93% of eProtocol-insulin instructions (11,773/12,645) in 100 adult and 48 pediatric subjects. Forty-eight percent of glucose values were in the target range. Both of these results met a priori-defined efficacy thresholds. Only 0.18% of glucose values were < or =40 mg/dl. This is lower than values reported in prior IIT studies. Although nurses reported eProtocol-insulin required as much work as managing a mechanical ventilator, most nurses felt eProtocol-insulin had a low impact on their ability to complete non-IIT nursing activities. CONCLUSIONS: A multicenter validation demonstrated that eProtocol-insulin is a valid, exportable tool that can assist clinicians in achieving control of glucose in critically ill adults and children.

Cell-specific expression of group X and group V secretory phospholipases A(2) in human lung airway epithelial cells.

Secretory phospholipase A(2) (sPLA(2)) enzymes contribute to inflammatory injury in human lungs by several mechanisms, including eicosanoid production and hydrolytic damage to surfactant phospholipids. Several distinct sPLA(2) genes have been described in human tissue but little is known regarding their presence, localization, or function(s) within lungs. We hypothesized that sPLA(2)s would have cell-specific distributions within lung. We used reverse transcriptase/polymerase chain reaction to identify sPLA(2) messenger RNAs (mRNAs) in adult human lung tissue. Resulting complementary DNA (cDNA) sequences indicated that total lung extracts contained mRNA for Groups IB, IIA, V, and X sPLA(2). An epithelial cell line, BEAS cells, expressed only Groups IIA, V, and X. We used these cDNAs to clone these enzymes, especially the recently described Group X and Group V enzymes. Digoxigenin-labeled complementary RNA probes were used to determine localization of each sPLA(2) by in situ hybridization of human lung. Hybridization was strongly positive for Group X and Group V in airway epithelial cells, which failed to hybridize Group IB or IIA probes. Although four known mammalian sPLA(2) isotypes were expressed in lung, only Group X and Group V sPLA(2) mRNAs appear uniquely expressed in airway epithelium, suggesting they could provide a mechanism of pulmonary surfactant hydrolysis during lung injury.