Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

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.

Standardizing Predicted Body Weight Equations for Mechanical Ventilation Tidal Volume Settings.

BACKGROUND: Recent recommendations for lung protective mechanical ventilation include a tidal volume target of 6 mL/kg predicted body weight (PBW). Different PBW equations might introduce important differences in tidal volumes delivered to research subjects and patients. METHODS: PBW equations use height, age, and sex as input variables. We compared National Institutes of Health (NIH) ARDS Network (ARDSNet), actuarial table (ACTUARIAL), and Stewart (STEWART) PBW equations used in clinical trials, across physiologic ranges for age and height. We used three-dimensional and two-dimensional surface analysis to compare these PBW equations. We then used age and height from actual clinical trial subjects to quantify PBW equation differences. RESULTS: Significant potential differences existed between these PBW predictions. The ACTUARIAL and ARDSNet surfaces for women were the only surfaces that intersected and produced both positive and negative differences. Mathematical differences between PBW equations at limits of height and age exceeded 30% in women and 24% in men for ACTUARIAL vs ARDSNet and about 25% for women and 15% for men for STEWART vs ARDSNet. The largest mathematical differences were present in older, shorter subjects, especially women. Actual differences for clinical trial subjects were as high as 15% for men and 24% for women. CONCLUSIONS: Significant differences between PBW equations for both men and women could be important sources of interstudy variation. Studies should adopt a standard PBW equation. We recommend using the NIH National Heart, Lung, and Blood Institute ARDS Network PBW equation because it is associated with the clinical trial that identified 6 mL/kg PBW as an appropriate target.

The evolution of eProtocols that enable reproducible clinical research and care methods.

Unnecessary variation in clinical care and clinical research reduces our ability to determine what healthcare interventions are effective. Reducing this unnecessary variation could lead to further healthcare quality improvement and more effective clinical research. We have developed and used electronic decision support tools (eProtocols) to reduce unnecessary variation. Our eProtocols have progressed from a locally developed mainframe computer application in one clinical site (LDS Hospital) to web-based applications available in multiple languages and used internationally. We use eProtocol-insulin as an example to illustrate this evolution. We initially developed eProtocol-insulin as a local quality improvement effort to manage stress hyperglycemia in the adult intensive care unit (ICU). We extended eProtocol-insulin use to translate our quality improvement results into usual clinical care at Intermountain Healthcare ICUs. We exported eProtocol-insulin to support research in other US and international institutions, and extended our work to the pediatric ICU. We iteratively refined eProtocol-insulin throughout these transitions, and incorporated new knowledge about managing stress hyperglycemia in the ICU. Based on our experience in the development and clinical use of eProtocols, we outline remaining challenges to eProtocol development, widespread distribution and use, and suggest a process for eProtocol development. Technical and regulatory issues, as well as standardization of protocol development, validation and maintenance, need to be addressed. Resolution of these issues should facilitate general use of eProtocols to improve patient care.

A closed-loop controller for mechanical ventilation of patients with ARDS.

Mechanical ventilators are routinely used to care for patients who cannot adequately breath on their own. Management of mechanical ventilation often involves a careful watch of the patient's arterial blood-oxygen tension and requires frequent adjustment of ventilation parameters to optimize the therapy. This situation lends itself as a candidate for closed-loop control. This report describes a closed-loop control system based on well-established protocols to systematically maintain appropriate levels of positive end-expiratory pressure (PEEP) and inspired oxygen (FiO2) in patients with Adult Respiratory Distress Syndrome (ARDS). The closed-loop control system consists of an in-dwelling arterial oxygenation (PaO2) sensor (Pfizer Continucath), coupled to a Macintosh computer that continuously controls FiO2 and PEEP settings on a Hamilton Amadeus ventilator. The implemented protocols provide continuous closed-loop control of oxygenation and a balance between patient need and minimal therapy. The controller is based on a traditional proportional-integral-derivative (PID) approach. The idea is to control, or maintain, the patient's PaO2 level at a target value determined, or set, by the patient's physician. The controller also features non-linear and adaptive characteristics that allow the system to respond more aggressively to "threatening" levels of PaO2. Another benefit of the control system is the ability to display, monitor, record and store all system parameters, settings, and control variables for future analysis and study. The system was extensively tested in the laboratory and in animal trials prior to use on human subjects. The results of a small clinical trial indicated that the system maintained control of the patient's therapy nearly 84% of the time. During the remainder of this time, the controller was interrupted primarily for suctioning, PaO2 sensor calibration or replacement. The response of the closed-loop controller was found to be appropriate, reliable and safe in patients with ARDS.