Despite the potential for electronic health records to help providers coordinate care, the current marketplace has failed to provide adequate solutions. Using a simple framework, we describe a vision of information technology capabilities that could substantially improve four care coordination activities: identifying collaborators, contacting collaborators, collaborating, and monitoring. Collaborators can include any individual clinician, caregiver, or provider organization involved in care for a given patient. This vision can be used to guide the development of care coordination tools and help policymakers track and promote their adoption.
care coordination; electronic health records; health information technology policy; Informatics research; IT Vision
Assess racial/ethnic and sex differences in treatment of vitamin D deficiency with high dose ergocalciferol (‘vitamin D2’) or other forms of vitamin D in a northeastern U.S. ambulatory clinic of an academic urban medical center.
Cross-sectional observational review of electronic medication prescribing records of patients with 25-hydroxyvitamin D (25OHD) deficiency (25OHD < 20 ng/ml) from 2004–2008.
Using multivariable logistic regression adjusting for patients’ demographics, and Elixhauser comorbidity score, we examined the association of sex and race/ethnicity with prescription for at least one dose of vitamin D.
Among 2,140 patients without renal disease and tested for 25OHD deficiency (25OHD < 20 ng/ml), 66.2% received no vitamin D prescription for vitamin D deficiency. Blacks and Hispanics received vitamin D prescriptions at a higher frequency than whites, 37.8% 38.4% and 30.9%, respectively, p=0.003. The vitamin D prescription rate for women versus men was 26.3% and 7.5%, respectively, p=0.04. In a fully adjusted model, no difference in prescription likelihood for blacks and whites [OR=1.18 95% CI, 0.88–1.58; p=0.29] or Hispanics and whites was noted [OR=1.01 95% CI, 0.70–1.45;p=0.73]. Similarly, fully adjusted model showed no difference in prescription likelihood for females and males [OR=1.23 95% CI, 0.93–1.63; p=0.12].
Among primary care patients with vitamin D deficiency, vitamin D supplementation was low and white patients were less likely to receive vitamin D treatment than blacks or Hispanics. Interventions to correct the high prevalence of vitamin D deficiency should address the markedly low rate of vitamin D prescribing when 25OHD levels are measured.
Vitamin D; electronic prescribing; ambulatory
The Leapfrog CPOE evaluation tool has been promoted as a means of monitoring computerized physician order entry (CPOE). We sought to determine the relationship between Leapfrog scores and the rates of preventable adverse drug events (ADE) and potential ADE.
Materials and methods
A cross-sectional study of 1000 adult admissions in five community hospitals from October 1, 2008 to September 30, 2010 was performed. Observed rates of preventable ADE and potential ADE were compared with scores reported by the Leapfrog CPOE evaluation tool. The primary outcome was the rate of preventable ADE and the secondary outcome was the composite rate of preventable ADE and potential ADE.
Leapfrog performance scores were highly related to the primary outcome. A 43% relative reduction in the rate of preventable ADE was predicted for every 5% increase in Leapfrog scores (rate ratio 0.57; 95% CI 0.37 to 0.88). In absolute terms, four fewer preventable ADE per 100 admissions were predicted for every 5% increase in overall Leapfrog scores (rate difference −4.2; 95% CI −7.4 to −1.1). A statistically significant relationship between Leapfrog scores and the secondary outcome, however, was not detected.
Our findings support the use of the Leapfrog tool as a means of evaluating and monitoring CPOE performance after implementation, as addressed by current certification standards.
Scores from the Leapfrog CPOE evaluation tool closely relate to actual rates of preventable ADE. Leapfrog testing may alert providers to potential vulnerabilities and highlight areas for further improvement.
medication safety; computerized physician order entry; clinical decision support; Leapfrog
We investigated incidence rates to understand the nature of medication errors potentially introduced by utilizing a computerized physician order entry (CPOE) system in the three clinical phases of the medication process: prescription, administration, and documentation.
Overt observations and chart reviews were employed at two surgical intensive care units of a 950-bed tertiary teaching hospital. Ten categories of high-risk drugs prescribed over a four-month period were noted and reviewed. Error definition and classifications were adapted from previous studies for use in the present research. Incidences of medication errors in the three phases of the medication process were analyzed. In addition, nurses' responses to prescription errors were also assessed.
Of the 534 prescriptions issued, 286 (53.6%) included at least one error. The proportion of errors was 19.0% (58) of the 306 drug administrations, of which two-thirds were verbal orders classified as errors due to incorrectly entered prescriptions. Documentation errors occurred in 205 (82.7%) of 248 correctly performed administrations. When tracking incorrectly entered prescriptions, 93% of the errors were intercepted by nurses, but two-thirds of them were recorded as prescribed rather than administered.
The number of errors occurring at each phase of the medication process was relatively high, despite long experience with a CPOE system. The main causes of administration errors and documentation errors were prescription errors and verbal order processes. To reduce these errors, hospital-level and unit-level efforts toward a better system are needed.
Although most outpatients are relatively healthy, many have chronic renal insufficiency, and high override rates for suggestions on renal dosing have been observed. To better understand the override of renal dosing alerts in an outpatient setting, we conducted a study to evaluate which patients were more frequently prescribed contraindicated medications, to assess providers’ responses to suggestions, and to examine the drugs involved and the reasons for overrides.
We obtained data on renal alert overrides and the coded reasons for overrides cited by providers at the time of prescription from outpatient clinics and ambulatory hospital-based practices at a large academic health care center over a period of 3 years, from January 2009 to December 2011. For detailed chart review, a group of 6 trained clinicians developed the appropriateness criteria with excellent inter-rater reliability (κ = 0.93). We stratified providers by override frequency and then drew samples from the high- and low-frequency groups. We measured the rate of total overrides, rate of appropriate overrides, medications overridden, and the reason(s) for override.
A total of 4120 renal alerts were triggered by 584 prescribers in the study period, among which 78.2% (3,221) were overridden. Almost half of the alerts were triggered by 40 providers and one-third was triggered by high-frequency overriders. The appropriateness rates were fairly similar, at 28.4% and 31.6% for high- and low-frequency overriders, respectively. Metformin, glyburide, hydrochlorothiazide, and nitrofurantoin were the most common drugs overridden. Physicians’ appropriateness rates were higher than the rates for nurse practitioners (32.9% vs. 22.1%). Physicians with low frequency override rates had higher levels of appropriateness for metformin than the high frequency overriders (P = 0.005).
A small number of providers accounted for a large fraction of overrides, as was the case with a small number of drugs. These data suggest that a focused intervention targeting primarily these providers and medications has the potential to improve medication safety.
Medication safety; Clinical decision support system; Renal insufficiency; Drug prescribing; Chronic kidney disease
There is growing interest in developing systems to overcome barriers for acquiring and interpreting family health histories in primary care.
To examine the capacity of three different electronic portals to collect family history from patients and deposit valid data in an electronic health record (EHR).
Patients were enrolled from four primary care practices and were asked to collect family health history before a physical exam using either telephone-based interactive voice response (IVR) technology, a secure internet portal, or a waiting room laptop computer, with portal assigned by practice. Intervention practices were compared to a “usual care” practice, where there was no standard workflow to document family history (663 participants in the three intervention arms were compared to 296 participants from the control practice).
New documentation of any family history in a coded EHR field within 30 days of the visit. Secondary outcomes included participation rates and validity.
Demographics varied by clinic. Documentation of new family history data was significantly higher, but modest, in each of the three intervention clinics (7.5 % for IVR clinic, 20.3 % for laptop clinic, and 23.1 % for patient portal clinic) versus the control clinic (1.7 %). Patient-entered data on common conditions in first degree relatives was confirmed as valid by a genetic counselor for the majority of cases (ranging from 64 to 82 % in the different arms).
Within primary care practices, valid patient entered family health history data can be obtained electronically at higher rates than a standard of care that depends on provider-entered data. Further research is needed to determine how best to match different portals to individual patient preference, how the tools can best be integrated with provider workflow, and to assess how they impact the use of screening and prevention.
Electronic supplementary material
The online version of this article (doi:10.1007/s11606-013-2442-0) contains supplementary material, which is available to authorized users.
family health history; electronic health record; EHR; provider-entered data
Few studies have focused on sepsis in patients with preexisting immunosuppression. Since their numbers and the incidence of sepsis are increasing, sepsis in immunosuppressed patients will increase in importance. We studied the epidemiology of sepsis and risk factors for 28-day mortality in patients immunosuppressed prior to the onset of sepsis using data from the Academic Medical Center Consortium’s (AMCC) prospective observational cohort study of sepsis. We compared characteristics of immunosuppressed (N=412) and immunocompetent (N=754) patients. Immunosuppressed patients were younger and more likely to have underlying liver or lung disease, and nosocomial infection or blood stream infection of unknown source when presenting with sepsis. They were also more likely to die within 28 days compared to immunocompetent patients (adjusted relative risk 1.62, 95% CI 1.38–1.91). Septic shock, hypothermia, cancer and invasive fungal infections were associated with increased mortality in immunosuppressed patients. Black race and the presence of rigors were independent predictors of survival in immunosuppressed patients. We conclude that sepsis among patients immunosuppressed prior to the onset of sepsis was associated with higher mortality than in immunocompetent patients. As the numbers of immunosuppressed patients continues to grow, more studies on the epidemiology of sepsis in this group will become increasingly important.
Patient safety; decision support; data exchange
Primary care clinicians can play an important role in identifying individuals at increased risk of cancer, but often do not obtain detailed information on family history or lifestyle factors from their patients.
We evaluated the feasibility and effectiveness of using a web-based risk appraisal tool in the primary care setting.
Five primary care practices within an academic care network were assigned to the intervention or control group.
We included 15,495 patients who had a new patient visit or annual exam during an 8-month period in 2010–2011.
Intervention patients were asked to complete a web-based risk appraisal tool on a laptop computer immediately before their visit. Information on family history of cancer was sent to their electronic health record (EHR) for clinicians to view; if accepted, it populated coded fields and could trigger clinician reminders about colon and breast cancer screening.
The main outcome measure was new documentation of a positive family history of cancer in coded EHR fields. Secondary outcomes included clinician reminders about screening and discussion of family history, lifestyle factors, and screening.
Among eligible intervention patients, 2.0 % had new information on family history of cancer entered in the EHR within 30 days after the visit, compared to 0.6 % of eligible control patients (adjusted odds ratio = 4.3, p = 0.03). There were no significant differences in the percent of patients who received moderate or high risk reminders for colon or breast cancer screening.
Use of this tool was associated with increased documentation of family history of cancer in the EHR, although the percentage of patients with new family history information was low in both groups. Further research is needed to determine how risk appraisal tools can be integrated with workflow and how they affect screening and health behaviors.
risk appraisal; family history; cancer; primary care
The implementation of health information technology interventions is at the forefront of most policy agendas internationally. However, such undertakings are often far from straightforward as they require complex strategic planning accompanying the systemic organizational changes associated with such programs. Building on our experiences of designing and evaluating the implementation of large-scale health information technology interventions in the USA and the UK, we highlight key lessons learned in the hope of informing the on-going international efforts of policymakers, health directorates, healthcare management, and senior clinicians.
Chronic kidney disease (CKD) is increasingly common and under-recognized in primary care clinics, leading to low rates of stage-appropriate monitoring and treatment. Our objective was to determine whether electronic problem list documentation of CKD is associated with monitoring and treatment.
This is a cross-sectional observational study of patients with stage 3 or 4 CKD, defined as two past estimated glomerular filtration rates (eGFR) 15-60 mL/min/1.73 m2 separated by 90 days and collected between 2007-2008. We examined the association of problem list documentation with: 1) serum eGFR monitoring test, 2) urine protein or albumin monitoring test, 3) an angiotensin converting enzyme inhibitor or angiotensin receptor blocker (ACE/ARB) prescription, 4) mean systolic blood pressure (BP), and 5) BP control.
Out of 3,149 patients with stage 3 or 4 CKD, only 16% of patients had CKD documented on the problem list. After adjustment for eGFR, gender, and race/ethnicity and after clustering by physician, problem list documentation of CKD was associated with serum eGFR testing (97% with problem list documentation vs. 94% without problem list documentation, p = 0.02) and urine protein testing (47% with problem list documentation vs. 40% without problem list documentation, p = 0.04). After adjustment, problem list documentation was not associated with ACE/ARB prescription, mean systolic BP, or BP control.
Documentation of CKD on the electronic problem list is rare. Patients with CKD documentation have better stage-appropriate monitoring of the disease, but do not have higher rates of blood pressure treatment or better blood pressure control. Interventions aimed at increasing documentation of CKD on the problem list may improve stage-appropriate monitoring, but may not improve clinical outcomes.
Electronic health record; Electronic problem list; Chronic kidney disease; Primary care; Electronic medical record
Alert fatigue represents a common problem associated with the use of clinical decision support systems in electronic health records (EHR). This problem is particularly profound with drug–drug interaction (DDI) alerts for which studies have reported override rates of approximately 90%. The objective of this study is to report consensus-based recommendations of an expert panel on DDI that can be safely made non-interruptive to the provider's workflow, in EHR, in an attempt to reduce alert fatigue.
We utilized an expert panel process to rate the interactions. Panelists had expertise in medicine, pharmacy, pharmacology and clinical informatics, and represented both academic institutions and vendors of medication knowledge bases and EHR. In addition, representatives from the US Food and Drug Administration and the American Society of Health-System Pharmacy contributed to the discussions.
Recommendations and considerations of the panel resulted in the creation of a list of 33 class-based low-priority DDI that do not warrant being interruptive alerts in EHR. In one institution, these accounted for 36% of the interactions displayed.
Development and customization of the content of medication knowledge bases that drive DDI alerting represents a resource-intensive task. Creation of a standardized list of low-priority DDI may help reduce alert fatigue across EHR.
Future efforts might include the development of a consortium to maintain this list over time. Such a list could also be used in conjunction with financial incentives tied to its adoption in EHR.
clinical decision support; medication alerts; drug-drug interactions; alert fatigue; DDI alerts; computerized decision support systems
Variability in medical practice in the United States leads to higher costs without achieving better patient outcomes. Clinical practice guidelines, which are intended to reduce variation and improve care, have several drawbacks that limit the extent of buy-in by clinicians. In contrast, standardized clinical assessment and management plans (SCAMPs) offer a clinician-designed approach to promoting care standardization that accommodates patients’ individual differences, respects providers’ clinical acumen, and keeps pace with the rapid growth of medical knowledge. Since early 2009 more than 12,000 patients have been enrolled in forty-nine SCAMPs in nine states and Washington, D.C. In one example, a SCAMP was credited with increasing clinicians’ rate of compliance with a recommended specialist referral for children from 19.6 percent to 75 percent. In another example, SCAMPs were associated with an 11–51 percent decrease in total medical expenses for six conditions when compared with a historical cohort. Innovative tools such as SCAMPs should be carefully examined by policy makers searching for methods to promote the delivery of high-quality, cost-effective care.
Despite mandates and incentives for electronic health record (EHR) adoption, little is known about factors predicting physicians’ satisfaction following EHR implementation.
To measure predictors of physician satisfaction following EHR adoption.
A total of 163 physicians completed a mailed survey before and after EHR implementation through a statewide pilot project in Massachusetts. Multivariable logistic regression identified predictors of physician satisfaction with their current practice situation in 2009 and generalized estimating equations accounted for clustering.
The response rate was 77% in 2005 and 68% in 2009. In 2005, prior to EHR adoption, 28% of physicians were very satisfied with their current practice situation compared to 25% in 2009, following EHR adoption (P < .001). In multivariate analysis, physician satisfaction following EHR adoption was correlated with self-reported ease of EHR implementation (adjusted odds ratio [OR] = 5.7, 95% CI 2.1 - 16), resources for practice improvement (adjusted OR = 2.6, 95% CI 1.2 - 6.1), pre-intervention satisfaction (adjusted OR = 4.8, 95% CI 1.5 - 15), and stress (adjusted OR = 5.3, 95% CI 1.1 - 25). Male physicians reported lower satisfaction following EHR adoption (adjusted OR = 0.3, 95% CI 0.2 - 0.6).
Interventions to expand EHR use should consider additional support for practices with fewer resources for improvement and ensure ease of EHR implementation. EHR adoption may be a factor in alleviating physicians’ stress. Addressing physicians’ satisfaction prior to practice transformation and anticipating greater dissatisfaction among male physicians will be essential to retaining the physician workforce and ensuring the quality of care they deliver.
electronic health record; physician satisfaction; implementation; Massachusetts eHealth collaborative
Personal health records (PHRs) have emerged as an important tool with which patients can electronically communicate with their doctors and doctor’s offices. However, there is a lack of theoretical and empirical research on how patients perceive the PHR and the differences in perceptions between users and non-users of the PHR.
To apply a theoretical model, the diffusion of innovation model, to the study of PHRs and conduct an exploratory empirical study on the applicability of the model to the study of perceptions of PHRs. A secondary objective was to assess whether perceptions of PHRs predict the perceived value of the PHR for communicating with the doctor’s office.
We first developed a survey capturing perceptions of PHR use and other factors such as sociodemographic characteristics, access and use of technology, perceived innovativeness in the domain of information technology, and perceptions of privacy and security. We then conducted a cross-sectional survey (N = 1500). Patients were grouped into five groups of 300: PHR users (innovators, other users, and laggards), rejecters, and non-adopters. We applied univariate statistical analysis (Pearson chi-square and one-way ANOVA) to assess differences among groups and used multivariate statistical techniques (factor analysis and multiple regression analysis) to assess the presence of factors identified by the diffusion of innovation model and the predictors of our dependent variable (value of PHR for communicating with the doctor’s office).
Of the 1500 surveys, 760 surveys were returned for an overall response rate of 51%. Computer use among non-adopters (75%) was lower than that among PHR users (99%) and rejecters (92%) (P < .001). Non-adopters also reported a lower score on personal innovativeness in information technology (mean = 2.8) compared to 3.6 and 3.1, respectively, for users and rejecters (P < .001). Four factors identified by the diffusion of innovation model emerged in the factor analysis: ease of use, relative advantage, observability, and trialability. PHR users perceived greater ease of use and relative advantage of the PHR than rejecters and non-adopters (P<.001). Multiple regression analysis showed the following factors as significant positive predictors of the value of PHR for communicating with the doctor’s office: relative advantage, ease of use, trialability, perceptions of privacy and security, age, and computer use.
Our study found that the diffusion of innovation model fits the study of perceptions of the PHR and provides a suitable theoretical and empirical framework to identify the factors that distinguish PHR users from non-users. The ease of use and relative advantage offered by the PHR emerged as the most important domains among perceptions of PHR use and in predicting the value of the PHR. Efforts to improve uptake and use of PHRs should focus on strategies that enhance the ease of use of PHRs and that highlight the relative advantages of PHRs.
Personal health record (PHR); perceptions; innovation; electronic health records (EHRs); meaningful use
At the 2011 American College of Medical Informatics (ACMI) Winter Symposium we studied the overlap between health IT and economics and what leading healthcare delivery organizations are achieving today using IT that might offer paths for the nation to follow for using health IT in healthcare reform. We recognized that health IT by itself can improve health value, but its main contribution to health value may be that it can make possible new care delivery models to achieve much larger value. Health IT is a critically important enabler to fundamental healthcare system changes that may be a way out of our current, severe problem of rising costs and national deficit. We review the current state of healthcare costs, federal health IT stimulus programs, and experiences of several leading organizations, and offer a model for how health IT fits into our health economic future.
Electronic Health Records/economics Health care Reform/trends*; cost-benefit analysis; information Systems/economics*; medical records systems; Computerized/economics; Developing/using computerized provider order entry; Natural-language processing; systems to support and improve diagnostic accuracy; other specific EHR applications (results review); patient safety; decision support; data exchange; system implementation and management issues; improving the education and skills training of health professionals; Developing/using clinical decision support (other than diagnostic) and guideline systems; Measuring/improving patient safety and reducing medical errors; clinical research informatics; information Retrieval; Collaborative technologies; methods for integration of information from disparate sources; Demonstrating return on IT investment; distributed systems; distributed systems
Clinical decision support systems can prevent knowledge-based prescription errors and improve patient outcomes. The clinical effectiveness of these systems, however, is substantially limited by poor user acceptance of presented warnings. To enhance alert acceptance it may be useful to quantify the impact of potential modulators of acceptance.
We built a logistic regression model to predict alert acceptance of drug–drug interaction (DDI) alerts in three different settings. Ten variables from the clinical and human factors literature were evaluated as potential modulators of provider alert acceptance. ORs were calculated for the impact of knowledge quality, alert display, textual information, prioritization, setting, patient age, dose-dependent toxicity, alert frequency, alert level, and required acknowledgment on acceptance of the DDI alert.
50 788 DDI alerts were analyzed. Providers accepted only 1.4% of non-interruptive alerts. For interruptive alerts, user acceptance positively correlated with frequency of the alert (OR 1.30, 95% CI 1.23 to 1.38), quality of display (4.75, 3.87 to 5.84), and alert level (1.74, 1.63 to 1.86). Alert acceptance was higher in inpatients (2.63, 2.32 to 2.97) and for drugs with dose-dependent toxicity (1.13, 1.07 to 1.21). The textual information influenced the mode of reaction and providers were more likely to modify the prescription if the message contained detailed advice on how to manage the DDI.
We evaluated potential modulators of alert acceptance by assessing content and human factors issues, and quantified the impact of a number of specific factors which influence alert acceptance. This information may help improve clinical decision support systems design.
Visualization of data and knowledge; knowledge representations; knowledge acquisition and knowledge management; distributed systems; agents; software engineering: architecture; developing and refining EHR data standards (including image standards); data models; data exchange; controlled terminologies and vocabularies; communication; integration across care settings (inter- and intra-enterprise); knowledge bases; electronic decision support; medication errors; patient safety; decision support
Health IT can play a major role in improving patient safety. Computerized physician order entry with decision support can alert providers to potential prescribing errors. However, too many alerts can result in providers ignoring and overriding clinically important ones.
To evaluate the appropriateness of providers’ drug-drug interaction (DDI) alert overrides, the reasons why they chose to override these alerts, and what actions they took as a consequence of the alert.
A cross-sectional, observational study of DDI alerts generated over a three-year period between January 1st, 2009, and December 31st, 2011.
Primary care practices affiliated with two Harvard teaching hospitals. The DDI alerts were screened to minimize the number of clinically unimportant warnings.
A total of 24,849 DDI alerts were generated in the study period, with 40% accepted. The top 62 providers with the highest override rate were identified and eight overrides randomly selected for each (a total of 496 alert overrides for 438 patients, 3.3% of the sample).
Overall, 68.2% (338/496) of the DDI alert overrides were considered appropriate. Among inappropriate overrides, the therapeutic combinations put patients at increased risk of several specific conditions including: serotonin syndrome (21.5%, n=34), cardiotoxicity (16.5%, n=26), or sharp falls in blood pressure or significant hypotension (28.5%, n=45). A small number of drugs and DDIs accounted for a disproportionate share of alert overrides. Of the 121 appropriate alert overrides where the provider indicated they would “monitor as recommended”, a detailed chart review revealed that only 35.5% (n=43) actually did. Providers sometimes reported that patients had already taken interacting medications together (15.7%, n=78), despite no evidence to confirm this.
Conclusions and Relevance
We found that providers continue to override important and useful alerts that are likely to cause serious patient injuries, even when relatively few false positive alerts are displayed.
Personal health records (PHRs) remain a relatively new technology and concept in practice even though they have been discussed in the literature for more than 50 years. There is no consensus on the definition of a PHR or PHR system even within the professional societies of health information technology.
Our objective was to analyze and classify the opinions of health information professionals regarding the definitions of the PHR.
Q methodology was used to explore the concept of the PHR. A total of 50 Q-statements were selected and rated by 45 P-samples consisting of health information professionals. We analyzed the resulting data by using Q methodology-specific software and SPSS.
We selected five types of health information professionals’ opinions: type I, public interest centered; type II, health information standardization centered; type III, health consumer centered; type IV, health information security centered; and type V, health consumer convenience centered. The Q-statements with the highest levels of agreement were as follows: (1) the PHR is the lifetime record of personal health information, (2) the PHR is the representation of health 2.0, and (3) security is the most important requirement of the PHR. The most disagreed-with Q-statements were (1) the PHR is a paper-based system, and (2) it is most effective to carry the PHR information in USB storage.
Health information professionals agree that PHRs should be lifetime records, that they will be useful as more information is stored electronically, and that data security is paramount. To maximize the benefits of PHR, activation strategies should be developed and extended across disciplines and professionals so that patients begin to receive the benefits associate with using PHRs.
Personal health record; P-sample; Q-sample; Q-statement; qualitative research; self-efficacy
There is a pressing need to understand the challenges surrounding procurement of and business case development for hospital electronic prescribing systems, and to identify possible strategies to enhance the efficiency of these processes in order to assist strategic decision making.
Materials and Methods
We organized eight multi-disciplinary round-table discussions in the United Kingdom. Participants included policy makers, representatives from hospitals, system developers, academics, and patients. Each discussion was digitally audio-recorded, transcribed verbatim and, together with accompanying field notes, analyzed thematically with NVivo9.
We drew on data from 17 participants (approximately eight per roundtable), six hours of discussion, and 15 pages of field notes. Key challenges included silo planning with systems not being considered as part of an integrated organizational information technology strategy, lack of opportunity for interactions between customers and potential suppliers, lack of support for hospitals in choosing appropriate systems, difficulty of balancing structured planning with flexibility, and the on-going challenge of distinguishing “wants” and aspirations from organizational “needs”.
Discussion and conclusions
Development of business cases for major investments in information technology does not take place in an organizational vacuum. Building on previously identified potentially transferable dimensions to the development and execution of business cases surrounding measurements of costs/benefits and risk management, we have identified additional components relevant to ePrescribing systems. These include: considerations surrounding strategic context, case for change and objectives, future service requirements and options appraisal, capital and revenue implications, timescale and deliverability, and risk analysis and management.
While some published research indicates a fairly high frequency of Intravenous (IV) medication errors associated with the use of smart infusion pumps, the generalizability of these results are uncertain. Additionally, the lack of a standardized methodology for measuring these errors is an issue. In this study we iteratively developed a web-based data collection tool to capture IV medication errors using a participatory design approach with interdisciplinary experts. Using the developed tool, a prevalence study was then conducted in an academic medical center. The results showed that the tool was easy to use and effectively captured all IV medication errors. Through the prevalence study, violation errors of hospital policy were found that could potentially place patients at risk, but no critical errors known to contribute to patient harm were noted.
To determine whether HIT currently supports care transitions we interviewed clinicians from several healthcare settings. We learned about HIT tools to help nurses facilitate transitions, but discovered that there are few tools to promote high quality, safe transitions of care. We also found that HIT is rarely employed for patient-centered care coordination mechanisms. In conclusion, HIT tools are typically used within one healthcare setting to prepare for a transition, rather than across healthcare settings.
electronic health record; meaningful use; care coordination; care transitions
Computerized provider order entry (CPOE) with clinical decision support (CDS) can help hospitals improve care. Little is known about what CDS is presently in use and how it is managed, however, especially in community hospitals. This study sought to address this knowledge gap by identifying standard practices related to CDS in US community hospitals with mature CPOE systems.
Materials and Methods
Representatives of 34 community hospitals, each of which had over 5 years experience with CPOE, were interviewed to identify standard practices related to CDS. Data were analyzed with a mix of descriptive statistics and qualitative approaches to the identification of patterns, themes and trends.
This broad sample of community hospitals had robust levels of CDS despite their small size and the independent nature of many of their physician staff members. The hospitals uniformly used medication alerts and order sets, had sophisticated governance procedures for CDS, and employed staff to customize CDS.
The level of customization needed for most CDS before implementation was greater than expected. Customization requires skilled individuals who represent an emerging manpower need at this type of hospital.
These results bode well for robust diffusion of CDS to similar hospitals in the process of adopting CDS and suggest that national policies to promote CDS use may be successful.
Clinical; decision support systems; medical order entry systems
Women with gestational diabetes (GDM) are at increased risk for type 2 diabetes (T2DM), but many do not receive recommended follow-up. We sought to identify barriers to follow-up screening.
We surveyed primary care (PCPs) and obstetric and gynecology care providers (OBCPs) in a large health system. We also assessed documentation of GDM history in the health care system’s electronic medical record.
478 clinicians were surveyed, among whom 207 responded. Most participants (81.1%) gave an accurate estimate of risk of progression to T2DM. PCPs were less likely than OBCPs to ask patients about history of GDM (OR 0.43, 95% CI 0.20–0.90), but they were far more likely to indicate that they order glucose screening for women with a known history (OR 4.31, 95% CI 2.01–9.26). Providers identified poor communication between OBCPs and PCPs as a major barrier to screening. Fewer than half (45.8%) of 450 women with GDM by GTT criteria had that history documented on their electronic problem list.
Clinicians are aware that women with GDM are at high risk of developing type 2 diabetes, but they do not routinely assess and screen patients, and communication between OBCPs and PCPs can be improved.
gestational diabetes; evidence-based practice; electronic medical record; type 2 diabetes