Electronic Health Record Transition Considerations.

After an initial phase of electronic health record (EHR) solutions for both independent practices and larger health care organizations, we are now entering a period in which transitioning between EHRs is becoming more common. Many of the decisions and implementation processes for an EHR transition are similar to those encountered during the transition from paper records to an EHR. Detailed project planning and management are essential to keep the effort on track and within budget to a successful conclusion. One major difference between the 2 situations is the possibility of migrating patient data by using automation. Understanding the implications of structured and unstructured data to manage the data migration between EHR systems is important to ensure success of the effort. Access to legacy data after the transition for both patient care and release of information to external parties is also critical to understand and manage proactively.

Clinical decision support of therapeutic drug monitoring of phenytoin: measured versus adjusted phenytoin plasma concentrations.

BACKGROUND: Therapeutic drug monitoring of phenytoin by measurement of plasma concentrations is often employed to optimize clinical efficacy while avoiding adverse effects. This is most commonly accomplished by measurement of total phenytoin plasma concentrations. However, total phenytoin levels can be misleading in patients with factors such as low plasma albumin that alter the free (unbound) concentrations of phenytoin. Direct measurement of free phenytoin concentrations in plasma is more costly and time-consuming than determination of total phenytoin concentrations. An alternative to direct measurement of free phenytoin concentrations is use of the Sheiner-Tozer equation to calculate an adjusted phenytoin that corrects for the plasma albumin concentration. Innovative medical informatics tools to identify patients who would benefit from adjusted phenytoin calculations or from laboratory measurement of free phenytoin are needed to improve safety and efficacy of phenytoin pharmacotherapy. The electronic medical record for an academic medical center was searched for the time period from August 1, 1996 to November 30, 2010 for patients who had total phenytoin and free phenytoin determined on the same blood draw, and also a plasma albumin measurement within 7 days of the phenytoin measurements. The measured free phenytoin plasma concentration was used as the gold standard. RESULTS: In this study, the standard Sheiner-Tozer formula for calculating an estimated (adjusted) phenytoin level more frequently underestimates than overestimates the measured free phenytoin relative to the respective therapeutic ranges. Adjusted phenytoin concentrations provided superior classification of patients than total phenytoin measurements, particularly at low albumin concentrations. Albumin plasma concentrations up to 7 days prior to total phenytoin measurements can be used for adjusted phenytoin concentrations. CONCLUSIONS: The results suggest that a measured free phenytoin should be obtained where possible to guide phenytoin dosing. If this is not feasible, then an adjusted phenytoin can supplement a total phenytoin concentration, particularly for patients with low plasma albumin.

Computerized detection of adverse drug reactions in the medical intensive care unit.

OBJECTIVE: Clinical event monitors are a type of active medication monitoring system that can use signals to alert clinicians to possible adverse drug reactions. The primary goal was to evaluate the positive predictive values of select signals used to automate the detection of ADRs in the medical intensive care unit. METHOD: This is a prospective, case series of adult patients in the medical intensive care unit during a six-week period who had one of five signals presents: an elevated blood urea nitrogen, vancomycin, or quinidine concentration, or a low sodium or glucose concentration. Alerts were assessed using 3 objective published adverse drug reaction determination instruments. An event was considered an adverse drug reaction when 2 out of 3 instruments had agreement of possible, probable or definite. Positive predictive values were calculated as the proportion of alerts that occurred, divided by the number of times that alerts occurred and adverse drug reactions were confirmed. RESULTS: 145 patients were eligible for evaluation. For the 48 patients (50% male) having an alert, the mean±SD age was 62±19 years. A total of 253 alerts were generated. Positive predictive values were 1.0, 0.55, 0.38 and 0.33 for vancomycin, glucose, sodium, and blood urea nitrogen, respectively. A quinidine alert was not generated during the evaluation. CONCLUSIONS: Computerized clinical event monitoring systems should be considered when developing methods to detect adverse drug reactions as part of intensive care unit patient safety surveillance systems, since they can automate the detection of these events using signals that have good performance characteristics by processing commonly available laboratory and medication information.

Analysis of a failed clinical decision support system for management of congestive heart failure.

In order to increase compliance with The Joint Commission's Congestive Heart Failure Core Measures, a rule based clinical decision support system (CDSS) was developed and deployed at a community hospital in our health system. We evaluated the performance of the CDSS in identifying patients with primary congestive heart failure (CHF)and identified problems encountered with its introduction. Performance of the CDSS was compared against a manual review of records of patients with diagnosis of primary CHF. The CDSS had a sensitivity of 0.79 and PPV of 0.11. The CDSS issued multiple alerts for majority of the patients(74%). The number of alerts issued for patients without primary CHF was large, and for a majority of patients (63%) physicians did not respond to alerts the first time. The CDSS performed poorly and was eventually withdrawn but provided insight into a subsequently successful method for managing CHF.

Negative CPOE attitudes correlate with diminished power in the workplace.

Power changes have been identified as a frequent and unintended consequence of the implementation of computerized physician order entry (CPOE). However, no previous study has described the degree or direction of power change, or even confirmed that such a relationship exists. Using a validated, standardized instrument for measuring personal power, we collected data from 276 healthcare workers in two different hospitals before and after implementation of CPOE. We identified a significant correlation between power perceptions and attitudes toward CPOE. Examining the direction of change by healthcare position, we found that the power perception values decreased for all positions and that attitudes toward CPOE varied based on use of the system. Understanding the relationship between power and CPOE is the first step in enabling systems developers to change the direction of power changes from negative to positive.

Development of an instrument for measuring clinicians' power perceptions in the workplace.

We report on the development of an instrument to measure clinicians' perceptions of their personal power in the workplace in relation to resistance to computerized physician order entry (CPOE). The instrument is based on French and Raven's six bases of social power and uses a semantic differential methodology. A measurement study was conducted to determine the reliability and validity of the survey. The survey was administered online and distributed via a URL by email to 19 physicians, nurses, and health unit coordinators from a university hospital. Acceptable reliability was achieved by removing or moving some semantic differential word pairs used to represent the six power bases (alpha range from 0.76 to 0.89). The Semantic Differential Power Perception (SDPP) survey validity was tested against an already validated instrument and found to be acceptable (correlation range from 0.51 to 0.81). The SDPP survey instrument was determined to be both reliable and valid.

Stroke-related knowledge and health behaviors among poststroke patients in inpatient rehabilitation.

OBJECTIVE: To measure stroke knowledge and prestroke personal health behaviors of stroke patients undergoing inpatient rehabilitation and their caregivers. DESIGN: Prospective cohort. SETTING: Academic rehabilitation hospital. PARTICIPANTS: A total of 130 stroke patients and 85 caregivers interviewed after ischemic stroke. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: The Stroke Education Assessment measured stroke knowledge and prestroke personal health behaviors. RESULTS: Large deficiencies in patient and caregiver stroke knowledge were found. Fifty-two percent of patients could not name any stroke risk factors, 52% were unable to name a stroke warning sign, and 35% were unable to identify appropriate actions to take in a stroke emergency. Older patients were less knowledgeable than younger patients. Caregivers were more knowledgeable than patients. Regarding prestroke personal health behaviors, 28% of patients reported medication nonadherence, 26% did not see their primary care physician in the preceding year, and less than 40% of patients with diabetes or hypertension reported diets consistent with these diagnoses. CONCLUSIONS: Stroke patients participating in inpatient rehabilitation and their caregivers have large gaps in stroke knowledge and have suboptimal personal health behaviors, thereby putting patients at high risk for recurrent stroke. Our finding highlights the need to develop stroke-education programs for rehabilitating patients that are effective in closing these gaps in knowledge and personal health behaviors.