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Background and Objectives: In an effort to improve and standardize the collection of adverse event data, the Agency for Healthcare Research and Quality is developing and testing a patient safety surveillance system called the Quality and Safety Review System (QSRS). Its current abstraction from medical records is through manual human coders, taking an average of 75 minutes to complete the review and abstraction tasks for one patient record. With many healthcare systems across the country adopting electronic health record (EHR) technology, there is tremendous potential for more efficient abstraction by automatically populating QSRS. In the absence of real-world testing data and models, which require a substantial investment, we provide a heuristic assessment of the feasibility of automatically populating QSRS questions from EHR data. Methods:To provide an assessment of the automation feasibility for QSRS, we first developed a heuristic framework, the Relative Abstraction Complexity Framework, to assess relative complexity of data abstraction questions. This framework assesses the relative complexity of characteristics or features of abstraction questions that should be considered when determining the feasibility of automating QSRS. Questions are assigned a final relative complexity score (RCS) of low, medium, or high by a team of clinicians, human factors, and natural language processing researchers.Results: One hundred thirty-four QSRS questions were coded using this framework by a team of natural language processing and clinical experts. Fifty-five questions (41%) had high RCS and would be more difficult to automate, such as "Was use of a device associated with an adverse outcome(s)?" Forty-two questions (31%) had medium RCS, such as "Were there any injuries as a result of the fall(s)?" and 37 questions (28%) had low RCS, such as "Did the patient deliver during this stay?" These results suggest that Blood and Hospital Acquired Infections-Clostridium Difficile Infection (HAI-CDI) modules would be relatively easier to automate, whereas Surgery and HAI-Surgical Site Infection would be more difficult to automate. Conclusions:Although EHRs contain a wealth of information, abstracting information from these records is still very challenging, particularly for complex questions, such as those concerning patient adverse events. In this work, we developed a heuristic framework, which can be applied to help guide conversations around the feasibility of automating QSRS data abstraction. This framework does not aim to replace testing with real data but complement the process by providing initial guidance and direction to subject matter experts to help prioritize, which abstraction questions to test for feasibility using real data.

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Surrogates development is important to extensively investigate the combustion behavior of fuels. Development of comprehensive surrogates has been focusing on matching chemical and physical properties of their target fuel to mimic its atomization, evaporation, mixing, and auto-ignition behavior. More focus has been given to matching the derived cetane number (DCN) as a measure of the auto-ignition quality. In this investigation, we carried out experimental validation of a three-component surrogate for Sasol-Isoparaffinic Kerosene (IPK) in ignition quality tester (IQT) and in an actual diesel engine. The surrogate fuel is composed of three components (46% iso-cetane, 44% decalin, and 10% n-nonane on a volume basis). The IQT experiments were conducted as per ASTM D6890-10a. The engine experiments were conducted at 1500 rpm, two engine loads, and two injection timings. Analysis of ignition delay (ID), peak pressure, peak rate of heat release (RHR), and other combustion phasing parameters showed a closer match in the IQT than in the diesel engine. Comparison between the surrogate combustion behavior in the diesel engine and IQT revealed that matching the DCN of the surrogate to its respective target fuel did not result in the same negative temperature coefficient (NTC) profile—which led to unmatched combustion characteristics in the high temperature combustion (HTC) regimes, despite the same auto-ignition and low temperature combustion (LTC) profiles. Moreover, a comparison between the combustion behaviors of the two fuels in the IQT is not consistent with the comparison in the diesel engine, which suggests that the surrogate validation in a single-cylinder diesel engine should be part of the surrogate development methodology, particularly for low ignition quality fuels.

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