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1.  Clinical decision making in a high-risk primary care environment: a qualitative study in the UK 
BMJ Open  2012;2(1):e000414.
Objective
Examine clinical reasoning and decision making in an out of hours (OOH) primary care setting to gain insights into how general practitioners (GPs) make clinical decisions and manage risk in this environment.
Design
Semi-structured interviews using open-ended questions.
Setting
A 2-month qualitative interview study conducted in Oxfordshire, UK.
Participants
21 GPs working in OOH primary care.
Results
The most powerful themes to emerge related to dealing with urgent potentially high-risk cases, keeping patients safe and responding to their needs, while trying to keep patients out of hospital and the concept of ‘fire fighting’. There were a number of well-defined characteristics that GPs reported making presentations easy or difficult to deal with. Severely ill patients were straightforward, while the older people, with complex multisystem diseases, were often difficult. GPs stopped collecting clinical information and came to clinical decisions when high-risk disease and severe illness requiring hospital attention has been excluded; they had responded directly to the patient's needs and there was a reliable safety net in place. Learning points that GPs identified as important for trainees in the OOH setting included the importance of developing rapport in spite of time pressures, learning to deal with uncertainty and learning about common presentations with a focus on critical cues to exclude severe illness.
Conclusions
The findings support suggestions that improvements in primary care OOH could be achieved by including automated and regular timely feedback system for GPs and individual peer and expert clinician support for GPs with regular meetings to discuss recent cases. In addition, trainee support and mentoring to focus on clinical skills, knowledge and risk management issues specific to OOH is currently required. Investigating the stopping rules used for diagnostic closure may provide new insights into the root causes of clinical error in such a high-risk setting.
Article summary
Article focus
Clinical reasoning and decision making in an out of hours (OOH) primary care setting.
The aim is to gain insights into how general practitioners (GPs) make clinical decisions and manage risk in this environment.
Implications for system changes and training.
Key messages
Clinical decision making in OOH is dominated by rule-out strategies for severe illness or potentially high-risk diseases.
GPs use three main criteria to determine diagnostic closure: global wellness with rule-outs, responded to patient needs, presence of a reliable safety net.
Improvements to clinical decision making could be achieved by providing routine feedback to clinical staff working in OOH, building in systems to support reflection on clinical cases and more tailored GP training.
Strengths and limitations of this study
The design of the study is based on a strong theoretical framework provided by the dual theory of cognition.
Face validity through using recently seen cases.
Limitations relate primarily to sampling, participants consisting of self-selected individuals.
doi:10.1136/bmjopen-2011-000414
PMCID: PMC3330259  PMID: 22318661
2.  The causes of prescribing errors in English general practices: a qualitative study 
The British Journal of General Practice  2013;63(615):e713-e720.
Background
Few detailed studies exist of the underlying causes of prescribing errors in the UK.
Aim
To examine the causes of prescribing and monitoring errors in general practice and provide recommendations for how they may be overcome.
Design and setting
Qualitative interview and focus group study with purposive sampling of English general practices.
Method
General practice staff from 15 general practices across three PCTs in England participated in a combination of semi-structured interviews (n = 34) and six focus groups (n = 46). Thematic analysis informed by Reason’s Accident Causation Model was used.
Results
Seven categories of high-level error-producing conditions were identified: the prescriber, the patient, the team, the working environment, the task, the computer system, and the primary–secondary care interface. These were broken down to reveal various error-producing conditions: the prescriber’s therapeutic training, drug knowledge and experience, knowledge of the patient, perception of risk, and their physical and emotional health; the patient’s characteristics and the complexity of the individual clinical case; the importance of feeling comfortable within the practice team was highlighted, as well as the safety implications of GPs signing prescriptions generated by nurses when they had not seen the patient for themselves; the working environment with its extensive workload, time pressures, and interruptions; and computer-related issues associated with mis-selecting drugs from electronic pick-lists and overriding alerts were all highlighted as possible causes of prescribing errors and were often interconnected.
Conclusion
Complex underlying causes of prescribing and monitoring errors in general practices were highlighted, several of which are amenable to intervention.
doi:10.3399/bjgp13X673739
PMCID: PMC3782805  PMID: 24152487
general practice; medication safety; patient safety; prescribing; primary care; quality
3.  Identification of factors associated with diagnostic error in primary care 
BMC Family Practice  2014;15:92.
Background
Missed, delayed or incorrect diagnoses are considered to be diagnostic errors. The aim of this paper is to describe the methodology of a study to analyse cognitive aspects of the process by which primary care (PC) physicians diagnose dyspnoea. It examines the possible links between the use of heuristics, suboptimal cognitive acts and diagnostic errors, using Reason’s taxonomy of human error (slips, lapses, mistakes and violations). The influence of situational factors (professional experience, perceived overwork and fatigue) is also analysed.
Methods
Cohort study of new episodes of dyspnoea in patients receiving care from family physicians and residents at PC centres in Granada (Spain). With an initial expected diagnostic error rate of 20%, and a sampling error of 3%, 384 episodes of dyspnoea are calculated to be required. In addition to filling out the electronic medical record of the patients attended, each physician fills out 2 specially designed questionnaires about the diagnostic process performed in each case of dyspnoea. The first questionnaire includes questions on the physician’s initial diagnostic impression, the 3 most likely diagnoses (in order of likelihood), and the diagnosis reached after the initial medical history and physical examination. It also includes items on the physicians’ perceived overwork and fatigue during patient care. The second questionnaire records the confirmed diagnosis once it is reached. The complete diagnostic process is peer-reviewed to identify and classify the diagnostic errors. The possible use of heuristics of representativeness, availability, and anchoring and adjustment in each diagnostic process is also analysed. Each audit is reviewed with the physician responsible for the diagnostic process. Finally, logistic regression models are used to determine if there are differences in the diagnostic error variables based on the heuristics identified.
Discussion
This work sets out a new approach to studying the diagnostic decision-making process in PC, taking advantage of new technologies which allow immediate recording of the decision-making process.
doi:10.1186/1471-2296-15-92
PMCID: PMC4024115  PMID: 24884984
Primary care; Diagnostic errors; Decision-making
4.  Effects of Two Commercial Electronic Prescribing Systems on Prescribing Error Rates in Hospital In-Patients: A Before and After Study 
PLoS Medicine  2012;9(1):e1001164.
In a before-and-after study, Johanna Westbrook and colleagues evaluate the change in prescribing error rates after the introduction of two commercial electronic prescribing systems in two Australian hospitals.
Background
Considerable investments are being made in commercial electronic prescribing systems (e-prescribing) in many countries. Few studies have measured or evaluated their effectiveness at reducing prescribing error rates, and interactions between system design and errors are not well understood, despite increasing concerns regarding new errors associated with system use. This study evaluated the effectiveness of two commercial e-prescribing systems in reducing prescribing error rates and their propensities for introducing new types of error.
Methods and Results
We conducted a before and after study involving medication chart audit of 3,291 admissions (1,923 at baseline and 1,368 post e-prescribing system) at two Australian teaching hospitals. In Hospital A, the Cerner Millennium e-prescribing system was implemented on one ward, and three wards, which did not receive the e-prescribing system, acted as controls. In Hospital B, the iSoft MedChart system was implemented on two wards and we compared before and after error rates. Procedural (e.g., unclear and incomplete prescribing orders) and clinical (e.g., wrong dose, wrong drug) errors were identified. Prescribing error rates per admission and per 100 patient days; rates of serious errors (5-point severity scale, those ≥3 were categorised as serious) by hospital and study period; and rates and categories of postintervention “system-related” errors (where system functionality or design contributed to the error) were calculated. Use of an e-prescribing system was associated with a statistically significant reduction in error rates in all three intervention wards (respectively reductions of 66.1% [95% CI 53.9%–78.3%]; 57.5% [33.8%–81.2%]; and 60.5% [48.5%–72.4%]). The use of the system resulted in a decline in errors at Hospital A from 6.25 per admission (95% CI 5.23–7.28) to 2.12 (95% CI 1.71–2.54; p<0.0001) and at Hospital B from 3.62 (95% CI 3.30–3.93) to 1.46 (95% CI 1.20–1.73; p<0.0001). This decrease was driven by a large reduction in unclear, illegal, and incomplete orders. The Hospital A control wards experienced no significant change (respectively −12.8% [95% CI −41.1% to 15.5%]; −11.3% [−40.1% to 17.5%]; −20.1% [−52.2% to 12.4%]). There was limited change in clinical error rates, but serious errors decreased by 44% (0.25 per admission to 0.14; p = 0.0002) across the intervention wards compared to the control wards (17% reduction; 0.30–0.25; p = 0.40). Both hospitals experienced system-related errors (0.73 and 0.51 per admission), which accounted for 35% of postsystem errors in the intervention wards; each system was associated with different types of system-related errors.
Conclusions
Implementation of these commercial e-prescribing systems resulted in statistically significant reductions in prescribing error rates. Reductions in clinical errors were limited in the absence of substantial decision support, but a statistically significant decline in serious errors was observed. System-related errors require close attention as they are frequent, but are potentially remediable by system redesign and user training. Limitations included a lack of control wards at Hospital B and an inability to randomize wards to the intervention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Medication errors—for example, prescribing the wrong drug or giving a drug by the wrong route—frequently occur in health care settings and are responsible for thousands of deaths every year. Until recently, medicines were prescribed and dispensed using systems based on hand-written scripts. In hospitals, for example, physicians wrote orders for medications directly onto a medication chart, which was then used by the nursing staff to give drugs to their patients. However, drugs are now increasingly being prescribed using electronic prescribing (e-prescribing) systems. With these systems, prescribers use a computer and order medications for their patients with the help of a drug information database and menu items, free text boxes, and prewritten orders for specific conditions (so-called passive decision support). The system reviews the patient's medication and known allergy list and alerts the physician to any potential problems, including drug interactions (active decision support). Then after the physician has responded to these alerts, the order is transmitted electronically to the pharmacy and/or the nursing staff who administer the prescription.
Why Was This Study Done?
By avoiding the need for physicians to write out prescriptions and by providing active and passive decision support, e-prescribing has the potential to reduce medication errors. But, even though many countries are investing in expensive commercial e-prescribing systems, few studies have evaluated the effects of these systems on prescribing error rates. Moreover, little is known about the interactions between system design and errors despite fears that e-prescribing might introduce new errors. In this study, the researchers analyze prescribing error rates in hospital in-patients before and after the implementation of two commercial e-prescribing systems.
What Did the Researchers Do and Find?
The researchers examined medication charts for procedural errors (unclear, incomplete, or illegal orders) and for clinical errors (for example, wrong drug or dose) at two Australian hospitals before and after the introduction of commercial e-prescribing systems. At Hospital A, the Cerner Millennium e-prescribing system was introduced on one ward; three other wards acted as controls. At Hospital B, the researchers compared the error rates on two wards before and after the introduction of the iSoft MedChart e-prescribing system. The introduction of an e-prescribing system was associated with a substantial reduction in error rates in the three intervention wards; error rates on the control wards did not change significantly during the study. At Hospital A, medication errors declined from 6.25 to 2.12 per admission after the introduction of e-prescribing whereas at Hospital B, they declined from 3.62 to 1.46 per admission. This reduction in error rates was mainly driven by a reduction in procedural error rates and there was only a limited change in overall clinical error rates. Notably, however, the rate of serious errors decreased across the intervention wards from 0.25 to 0.14 per admission (a 44% reduction), whereas the serious error rate only decreased by 17% in the control wards during the study. Finally, system-related errors (for example, selection of an inappropriate drug located on a drop-down menu next to a likely drug selection) accounted for 35% of errors in the intervention wards after the implementation of e-prescribing.
What Do These Findings Mean?
These findings show that the implementation of these two e-prescribing systems markedly reduced hospital in-patient prescribing error rates, mainly by reducing the number of incomplete, illegal, or unclear medication orders. The limited decision support built into both the e-prescribing systems used here may explain the limited reduction in clinical error rates but, importantly, both e-prescribing systems reduced serious medication errors. Finally, the high rate of system-related errors recorded in this study is worrying but is potentially remediable by system redesign and user training. Because this was a “real-world” study, it was not possible to choose the intervention wards randomly. Moreover, there was no control ward at Hospital B, and the wards included in the study had very different specialties. These and other aspects of the study design may limit the generalizability of these findings, which need to be confirmed and extended in additional studies. Even so, these findings provide persuasive evidence of the current and potential ability of commercial e-prescribing systems to reduce prescribing errors in hospital in-patients provided these systems are continually monitored and refined to improve their performance.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001164.
ClinfoWiki has pages on medication errors and on electronic prescribing (note: the Clinical Informatics Wiki is a free online resource that anyone can add to or edit)
Electronic prescribing in hospitals challenges and lessons learned describes the implementation of e-prescribing in UK hospitals; more information about e-prescribing in the UK is available on the NHS Connecting for Health Website
The Clinicians Guide to e-Prescribing provides up-to-date information about e-prescribing in the USA
Information about e-prescribing in Australia is also available
Information about electronic health records in Australia
doi:10.1371/journal.pmed.1001164
PMCID: PMC3269428  PMID: 22303286
5.  Patient-Safety-Related Hospital Deaths in England: Thematic Analysis of Incidents Reported to a National Database, 2010–2012 
PLoS Medicine  2014;11(6):e1001667.
Sukhmeet Panesar and colleagues classified reports of patient-safety-related hospital deaths in England to identify patterns of cases where improvements might be possible.
Please see later in the article for the Editors' Summary
Background
Hospital mortality is increasingly being regarded as a key indicator of patient safety, yet methodologies for assessing mortality are frequently contested and seldom point directly to areas of risk and solutions. The aim of our study was to classify reports of deaths due to unsafe care into broad areas of systemic failure capable of being addressed by stronger policies, procedures, and practices. The deaths were reported to a patient safety incident reporting system after mandatory reporting of such incidents was introduced.
Methods and Findings
The UK National Health Service database was searched for incidents resulting in a reported death of an adult over the period of the study. The study population comprised 2,010 incidents involving patients aged 16 y and over in acute hospital settings. Each incident report was reviewed by two of the authors, and, by scrutinising the structured information together with the free text, a main reason for the harm was identified and recorded as one of 18 incident types. These incident types were then aggregated into six areas of apparent systemic failure: mismanagement of deterioration (35%), failure of prevention (26%), deficient checking and oversight (11%), dysfunctional patient flow (10%), equipment-related errors (6%), and other (12%). The most common incident types were failure to act on or recognise deterioration (23%), inpatient falls (10%), healthcare-associated infections (10%), unexpected per-operative death (6%), and poor or inadequate handover (5%). Analysis of these 2,010 fatal incidents reveals patterns of issues that point to actionable areas for improvement.
Conclusions
Our approach demonstrates the potential utility of patient safety incident reports in identifying areas of service failure and highlights opportunities for corrective action to save lives.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Being admitted to the hospital is worrying for patients and for their relatives. Will the patient recover or die in the hospital? Some seriously ill patients will inevitably die, but in an ideal world, no one should die in the hospital because of inadequate or unsafe care (an avoidable death). No one should die, for example, because healthcare professionals fail to act on signs that indicate a decline in a patient's clinical condition. Hospital mortality (death) is often regarded as a key indicator of patient safety in hospitals, and death rate indicators such as the “hospital standardized mortality ratio” (the ratio of the actual number of acute in-hospital deaths to the expected number of in-hospital deaths) are widely used to monitor and improve hospital safety standards. In England, for example, a 2012 report that included this measure as an indicator of hospital performance led to headlines of “worryingly high” hospital death rates and to a review of the quality of care in the hospitals with the highest death rates.
Why Was This Study Done?
Hospital standardized mortality ratios and other measures of in-patient mortality can be misleading because they can, for example, reflect the burden of disease near the hospital rather than the hospital's quality of care or safety levels. Moreover, comparative data on hospital mortality rates are of limited value in identifying areas of risk to patients or solutions to the problem of avoidable deaths. In this study, to identify areas of service failure amenable to improvement through strengthened clinical policies, procedures, and practices, the researchers undertake a thematic analysis of deaths in hospitals in England that were reported by healthcare staff to a mandatory patient-safety-related incident reporting system. Since 2004, staff in the UK National Health Service (the NHS comprises the publicly funded healthcare systems in England, Scotland, Wales, and Northern Ireland) have been encouraged to report any unintended or unexpected incident in which they believe a patient's safety was compromised. Since June 2010, it has been mandatory for staff in England and Wales to report deaths due to patient-safety-related incidents. A thematic analysis examines patterns (“themes”) within nonnumerical (qualitative) data.
What Did the Researchers Do and Find?
By searching the NHS database of patient-safety-related incidents, the researchers identified 2010 incidents that occurred between 1 June 2010 and 31 October 2012 that resulted in the death of adult patients in acute hospital settings. By scrutinizing the structured information in each incident report and the associated free text in which the reporter described what happened and why they think it happened, the researchers classified the reports into 18 incident categories. These categories fell into six broad areas of systemic failure—mismanagement of deterioration (35% of incidents), failure of prevention (26%), deficient checking and oversight (11%), dysfunctional patient flow (10%), equipment-related errors (6%), and other (12%, incidents where the problem underlying death was unclear). Management of deterioration, for example, included the incident categories “failure to act on or recognize deterioration” (23% of reported incidents), “failure to give ordered treatment/support in a timely manner,” and “failure to observe.” Failure of prevention included the incident categories “falls” (10% of reported incidents), “healthcare-associated infections” (also 10% of reported incidents), “pressure sores,” “suicides,” and “deep vein thrombosis/pulmonary embolism.”
What Do These Findings Mean?
Although the accuracy of these findings may be limited by data quality and by other aspects of the study design, they reveal patterns of patient-safety-related deaths in hospitals in England and highlight areas of healthcare that can be targeted for improvement. The finding that the mismanagement of deterioration of acutely ill patients is involved in a third of patient-safety-related deaths identifies an area of particular concern in the NHS and, potentially, in other healthcare systems. One way to reduce deaths associated with the mismanagement of deterioration, suggest the researchers, might be to introduce a standardized early warning score to ensure uniform identification of this population of patients. The researchers also suggest that more effort should be put into designing programs to prevent falls and other incidents and into ensuring that these programs are effectively implemented. More generally, the classification system developed here has the potential to help hospital boards and clinicians identify areas of patient care that require greater scrutiny and intervention and thereby save the lives of many hospital patients.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001667.
The NHS provides information about patient safety, including a definition of a patient safety incident and information on reporting patient safety incidents
The NHS Choices website includes several “Behind the Headlines” articles that discuss patient safety in hospitals, including an article that discusses the 2012 report of high hospital death rates in England, “Fit for the Future?” and another that discusses the Keogh review of the quality of care in the hospitals with highest death rates
The US Agency for Healthcare Research and Quality provides information on patient safety in the US
Wikipedia has pages on thematic analysis and on patient safety (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
doi:10.1371/journal.pmed.1001667
PMCID: PMC4068985  PMID: 24959751
6.  The Effect of Automated Alerts on Provider Ordering Behavior in an Outpatient Setting 
PLoS Medicine  2005;2(9):e255.
Background
Computerized order entry systems have the potential to prevent medication errors and decrease adverse drug events with the use of clinical-decision support systems presenting alerts to providers. Despite the large volume of medications prescribed in the outpatient setting, few studies have assessed the impact of automated alerts on medication errors related to drug–laboratory interactions in an outpatient primary-care setting.
Methods and Findings
A primary-care clinic in an integrated safety net institution was the setting for the study. In collaboration with commercial information technology vendors, rules were developed to address a set of drug–laboratory interactions. All patients seen in the clinic during the study period were eligible for the intervention. As providers ordered medications on a computer, an alert was displayed if a relevant drug–laboratory interaction existed. Comparisons were made between baseline and postintervention time periods. Provider ordering behavior was monitored focusing on the number of medication orders not completed and the number of rule-associated laboratory test orders initiated after alert display. Adverse drug events were assessed by doing a random sample of chart reviews using the Naranjo scoring scale.
The rule processed 16,291 times during the study period on all possible medication orders: 7,017 during the pre-intervention period and 9,274 during the postintervention period. During the postintervention period, an alert was displayed for 11.8% (1,093 out of 9,274) of the times the rule processed, with 5.6% for only “missing laboratory values,” 6.0% for only “abnormal laboratory values,” and 0.2% for both types of alerts. Focusing on 18 high-volume and high-risk medications revealed a significant increase in the percentage of time the provider stopped the ordering process and did not complete the medication order when an alert for an abnormal rule-associated laboratory result was displayed (5.6% vs. 10.9%, p = 0.03, Generalized Estimating Equations test). The provider also increased ordering of the rule-associated laboratory test when an alert was displayed (39% at baseline vs. 51% during post intervention, p < 0.001). There was a non-statistically significant difference towards less “definite” or “probable” adverse drug events defined by Naranjo scoring (10.3% at baseline vs. 4.3% during postintervention, p = 0.23).
Conclusion
Providers will adhere to alerts and will use this information to improve patient care. Specifically, in response to drug–laboratory interaction alerts, providers will significantly increase the ordering of appropriate laboratory tests. There may be a concomitant change in adverse drug events that would require a larger study to confirm. Implementation of rules technology to prevent medication errors could be an effective tool for reducing medication errors in an outpatient setting.
A computerized order entry system that alerted providers to potential problems was shown to be able to influence prescribing practice
doi:10.1371/journal.pmed.0020255
PMCID: PMC1198038  PMID: 16128621
7.  Diagnostic Errors in Pediatric Echocardiography Development of Taxonomy and Identification of Risk Factors 
Circulation  2008;117(23):2995-3001.
Background
Despite increased interest in complications within pediatric cardiology, the domain of imaging-related diagnostic errors has received little attention. We developed a new taxonomy for diagnostic errors within pediatric echocardiography that categorizes errors by severity, preventability, and primary contributor. Our objectives were to examine its findings when applied to diagnostic error cases and to identify risk factors for preventable or possibly preventable diagnostic errors.
Methods and Results
Diagnostic errors were identified at a high-volume academic pediatric cardiac center from December 2004 to August 2007. Demographic, clinical, and situational variables were collected from these cases and controls. During the study period, ≈50 660 echocardiograms were performed. Among the 87 diagnostic error cases identified, 70% affected clinical management or the patient was at risk of or experienced an adverse event. One third of the errors were preventable and 46% were possibly preventable; 69% of preventable errors were of moderate severity or greater. Univariate analysis demonstrated that preventable or possibly preventable errors were more likely to involve younger patients, lower body weight, study location, sedated/anesthetized patients, studies performed and interpreted at night, uncommon diagnoses, and greater anatomic complexity than controls. Multivariate analysis identified the following risk factors: rare or very rare diagnoses (adjusted odds ratio [AOR], 9.2; P<0.001), study location in the recovery room (AOR, 7.9; P<0.001), moderate anatomic complexity (AOR, 3.5; P=0.004), and patient weight <5 kg (AOR, 3.5; P=0.031).
Conclusions
A diagnostic error taxonomy and knowledge of risk factors can assist in identification of targets for quality improvement initiatives that aim to decrease diagnostic error in pediatric echocardiography.
doi:10.1161/CIRCULATIONAHA.107.758532
PMCID: PMC4237021  PMID: 18519849
diagnostic errors; echocardiography; congenital heart disease; pediatrics
8.  Can an electronic prescribing system detect doctors who are more likely to make a serious prescribing error? 
Objectives
We aimed to assess whether routine data produced by an electronic prescribing system might be useful in identifying doctors at higher risk of making a serious prescribing error.
Design
Retrospective analysis of prescribing by junior doctors over 12 months using an electronic prescribing information and communication system. The system issues a graded series of prescribing alerts (low-level, intermediate, and high-level), and warnings and prompts to respond to abnormal test results. These may be overridden or heeded, except for high-level prescribing alerts, which are indicative of a potentially serious error and impose a ‘hard stop’.
Setting
A large teaching hospital.
Participants
All junior doctors in the study setting.
Main outcome measures
Rates of prescribing alerts and laboratory warnings and doctors' responses.
Results
Altogether 848,678 completed prescriptions issued by 381 doctors (median 1538 prescriptions per doctor, interquartile range [IQR] 328–3275) were analysed. We identified 895,029 low-level alerts (median 1033 per 1000 prescriptions per doctor, IQR 903–1205) with a median of 34% (IQR 31–39%) heeded; 172,434 intermediate alerts (median 196 per 1000 prescriptions per doctor, IQR 159–266), with a median of 23% (IQR 16–30%) heeded; and 11,940 high-level ‘hard stop’ alerts. Doctors vary greatly in the extent to which they trigger and respond to alerts of different types. The rate of high-level alerts showed weak correlation with the rate of intermediate prescribing alerts (correlation coefficient, r = 0.40, P = <0.001); very weak correlation with low-level alerts (r = 0.12, P = 0.019); and showed weak (and sometimes negative) correlation with propensity to heed test-related warnings or alarms. The degree of correlation between generation of intermediate and high-level alerts is insufficient to identify doctors at high risk of making serious errors.
Conclusions
Routine data from an electronic prescribing system should not be used to identify doctors who are at risk of making serious errors. Careful evaluation of the kinds of quality assurance questions for which routine data are suitable will be increasingly valuable.
doi:10.1258/jrsm.2011.110061
PMCID: PMC3089874  PMID: 21558099
9.  Types and Origins of Diagnostic Errors in Primary Care Settings 
JAMA internal medicine  2013;173(6):418-425.
Importance
Diagnostic errors are an understudied aspect of ambulatory patient safety.
Objectives
To determine the types of diseases missed and the diagnostic process involved in the cases of confirmed diagnosis errors in primary care settings and to determine whether record reviews could shed light on potential contributory factors to inform future interventions.
Design
We reviewed medical records of diagnostic errors detected at two sites through electronic health records-based triggers. Triggers were based on patterns of patients’ unexpected return visits after an initial primary care “index” visit.
Setting
A larger urban Veterans Affairs facility and a large integrated private health care system.
Participants
Our study focused on 190 unique instances of diagnostic errors detected in primary care visits between October 1, 2006, and September 30, 2007.
Main Outcome Measures
Through medical record reviews, we collected data on presenting symptoms at the index visit, types of diagnoses missed, process breakdowns, potential contributory factors, and potential for harm from errors.
Results
In 190 cases, a total of 68 unique diagnoses were missed. Most missed diagnoses were common conditions in primary care, with pneumonia (6.7%), decompensated congestive heart failure (5.7%), acute renal failure (5.3%), cancer (primary) (5.3%), and urinary tract infection or pyelonephritis (4.8%) being most common. Process breakdowns most frequently involved the patient-practitioner clinical encounter (78.9%) but were also related to referrals (19.5%), patient-related factors (16.3%), follow-up and tracking of diagnostic information (14.7%), and performance and interpretation of diagnostic tests (13.6%). A total of 43.7% of cases involved more than one of these processes. Patient- practitioner encounter breakdowns were primarily related to problems with history-taking (56.3%), examination (47.4%), and/or ordering diagnostic tests for further work-up (57.4%). Most errors were associated with potential for moderate-to-severe harm.
Conclusions and Relevance
Diagnostic errors identified in our study involved a large variety of common diseases and had significant potential for harm. Most errors were related to process breakdowns in the patient-practitioner clinical encounter. Preventive interventions should target common contributory factors across diagnoses, especially those that involve data gathering and synthesis in the patient- practitioner encounter.
doi:10.1001/jamainternmed.2013.2777
PMCID: PMC3690001  PMID: 23440149
diagnostic errors; patient safety; follow-up; primary care; electronic health records
10.  Exploring Situational Awareness in Diagnostic Errors in Primary Care 
BMJ quality & safety  2011;21(1):30-38.
Objective
Diagnostic errors in primary care are harmful but poorly studied. To facilitate understanding of diagnostic errors in real-world primary care settings using electronic health records (EHRs), this study explored the use of the Situational Awareness (SA) framework from aviation human factors research.
Methods
A mixed-methods study was conducted involving reviews of EHR data followed by semi-structured interviews of selected providers from two institutions in the US. The study population included 380 consecutive patients with colorectal and lung cancers diagnosed between February 2008 and January 2009. Using a pre-tested data collection instrument, trained physicians identified diagnostic errors, defined as lack of timely action on one or more established indications for diagnostic work-up for lung and colorectal cancers. Twenty-six providers involved in cases with and without errors were interviewed. Interviews probed for providers' lack of SA and how this may have influenced the diagnostic process.
Results
Of 254 cases meeting inclusion criteria, errors were found in 30 (32.6%) of 92 lung cancer cases and 56 (33.5%) of 167 colorectal cancer cases. Analysis of interviews related to error cases revealed evidence of lack of one of four levels of SA applicable to primary care practice: information perception, information comprehension, forecasting future events, and choosing appropriate action based on the first three levels. In cases without error, the application of the SA framework provided insight into processes involved in attention management.
Conclusions
A framework of SA can help analyze and understand diagnostic errors in primary care settings that use EHRs.
doi:10.1136/bmjqs-2011-000310
PMCID: PMC3692739  PMID: 21890757
diagnostic error; decision-making; patient safety; primary care; medical errors; human factors; cancer; electronic health records; diagnostic delays
11.  Stroke awareness in the general population: knowledge of stroke risk factors and warning signs in older adults 
BMC Geriatrics  2009;9:35.
Background
Stroke is a leading cause of death and functional impairment. While older people are particularly vulnerable to stroke, research suggests that they have the poorest awareness of stroke warning signs and risk factors. This study examined knowledge of stroke warning signs and risk factors among community-dwelling older adults.
Methods
Randomly selected community-dwelling older people (aged 65+) in Ireland (n = 2,033; 68% response rate). Participants completed home interviews. Questions assessed knowledge of stroke warning signs and risk factors, and personal risk factors for stroke.
Results
Of the overall sample, 6% had previously experienced a stroke or transient ischaemic attack. When asked to identify stroke risk factors from a provided list, less than half of the overall sample identified established risk factors (e.g., smoking, hypercholesterolaemia), hypertension being the only exception (identified by 74%). Similarly, less than half identified established warning signs (e.g., weakness, headache), with slurred speech (54%) as the exception. Overall, there were considerable gaps in awareness with poorest levels evident in those with primary level education only and in those living in Northern Ireland (compared with Republic of Ireland).
Conclusion
Knowledge deficits in this study suggest that most of the common early symptoms or signs of stroke were recognized as such by less than half of the older adults surveyed. As such, many older adults may not recognise early symptoms of stroke in themselves or others. Thus, they may lose vital time in presenting for medical attention. Lack of public awareness about stroke warning signs and risk factors must be addressed as one important contribution to reducing mortality and morbidity from stroke.
doi:10.1186/1471-2318-9-35
PMCID: PMC2734750  PMID: 19656359
12.  A preliminary taxonomy of medical errors in family practice 
Quality & safety in health care  2002;11(3):233-238.
Objective: To develop a preliminary taxonomy of primary care medical errors.
Design: Qualitative analysis to identify categories of error reported during a randomized controlled trial of computer and paper reporting methods.
Setting: The National Network for Family Practice and Primary Care Research.
Participants: Family physicians.
Main outcome measures: Medical error category, context, and consequence.
Results: Forty two physicians made 344 reports: 284 (82.6%) arose from healthcare systems dysfunction; 46 (13.4%) were errors due to gaps in knowledge or skills; and 14 (4.1%) were reports of adverse events, not errors. The main subcategories were: administrative failures (102; 30.9% of errors), investigation failures (82; 24.8%), treatment delivery lapses (76; 23.0%), miscommunication (19; 5.8%), payment systems problems (4; 1.2%), error in the execution of a clinical task (19; 5.8%), wrong treatment decision (14; 4.2%), and wrong diagnosis (13; 3.9%). Most reports were of errors that were recognized and occurred in reporters' practices. Affected patients ranged in age from 8 months to 100 years, were of both sexes, and represented all major US ethnic groups. Almost half the reports were of events which had adverse consequences. Ten errors resulted in patients being admitted to hospital and one patient died.
Conclusions: This medical error taxonomy, developed from self-reports of errors observed by family physicians during their routine clinical practice, emphasizes problems in healthcare processes and acknowledges medical errors arising from shortfalls in clinical knowledge and skills. Patient safety strategies with most effect in primary care settings need to be broader than the current focus on medication errors.
doi:10.1136/qhc.11.3.233
PMCID: PMC1743626  PMID: 12486987
13.  Telehealth research 
Aims
Oxleas Telehealth is piloting the effectiveness of remotely monitoring Tier 3 COPD1 patients to reduce the hospital admissions and enable community nurses to reach more patients. Setting alert thresholds for clinical readings, which indicate an ill patient if exceeded, is crucial to remote monitoring. Hitherto, thresholds have been set from national guidelines. Patient specific adjustments are then made upon periodic qualitative assessments of progress. This causes the following problems:
Excessive red alerts taking long time to clinically triage
No standardised method for adjusting thresholds
Anticipated operational issues when upscaling
Oxleas is researching a quantitative method for setting thresholds by statistically analysing daily variation in patients’ clinical measurements, to differentiate between common place variation in physiology, and special case variation due to ill-health.
Potential benefits
Reduced nurse time spent triaging data and contacting patients with red alerts but well
Freed up nursing time to deliver more community care to patients
Efficient method for threshold setting when upscaling
Cycle 1 method
For each patient, pulse, oxygen and blood pressure readings were collated for the two preceding months. Arithmetic means were calculated for each. Spread analysis confirmed a bell shaped distribution. Alert thresholds were set three standard deviations from mean for each parameter. Exceptions were made for variation indicating good health (e.g. high oxygen levels). Episodes of abnormal readings were compared to clinical notes to find correlation with episodes of illness.
Cycle 1 result
Correlation revealed 100% sensitivity, where all episodes of illness produced measurements exceeding thresholds. Specificity was poor (85% for oxygen, 60% for pulse and 52% for blood pressure) with numerous false positive readings. Of false positives, 61% occurred due to technical error (mostly faulty batteries or poor user technique). False positives for non-technical reasons were 78% for blood pressure, 57% for pulse and 20% for oxygen.
Cycle 2 method
This was performed to audit technical error. From cycle 1 results, eliminating technical error would improve specificity to 86% for oxygen, 73% for pulse and 63% for blood pressure. Drop down menus were installed on the triage software to code for:
Number of abnormal measurements daily
Number of technical errors daily (separate codes for battery error and patient user errors)
These were selected daily for each patient. Concurrently, improved systems for battery replacement have been implemented. Patient training has been provided when indicated. Alert thresholds have been updated as in cycle one.
Cycle 2 result (due December)
Early signs of reduced triage time and fewer red alerts will be quantified.
Conclusions
Statistical analysis of measurement variation can provide a highly sensitive method for setting alert thresholds that accurately identifying illness however technical need reduced to improve specificity. The method is most effective for oxygen saturation. Improving effectiveness for other parameters may require improved patient training for equipment use (e.g. activities immediately prior to taking readings etc.). Research cycles using narrower standard deviations to improve specificity are required. If high sensitivity and specificity can be achieved, method can be used to automatically adjust thresholds periodically, reflecting the patient’s most recent clinical status.
PMCID: PMC3571163
telehealth; COPD
14.  Seen Through Their Eyes: Residents’ Reflections on the Cognitive and Contextual Components of Diagnostic Errors in Medicine 
Purpose
Diagnostic errors in medicine are common and costly. Cognitive bias causes are increasingly recognized contributors to diagnostic error, but remain difficult targets for medical educators and patient safety experts. The authors explored the cognitive and contextual components of diagnostic errors described by internal medicine resident physicians through the use of an educational intervention.
Method
Forty-one internal medicine residents at University of Pennsylvania participated in an educational intervention in 2010 comprised of reflective writing and facilitated small group discussion about experiences with diagnostic error from cognitive bias. Narratives and discussion were transcribed and analyzed iteratively to identify types of cognitive bias and contextual factors present.
Results
All residents described a personal experience with a case of diagnostic error that contained at least one cognitive bias and one contextual factor that may have influenced the outcome. The most common cognitive biases identified by the residents were anchoring bias (36, or 88%), availability bias (31, 76%), and framing effect (23, 56%). Prominent contextual factors included caring for patients on a subspecialty service (31, or 76%), complex illness (26, 63%), and time pressures (22, 54%). Eighty-five percent of residents described at least one strategy to avoid a similar error in the future.
Conclusions
Residents can easily recall diagnostic errors, analyze the errors for cognitive bias, and richly describe their context. The use of reflective writing and narrative discussion is an educational strategy to teach recognition, analysis, and cognitive bias avoidance strategies for diagnostic error in residency education.
doi:10.1097/ACM.0b013e31826742c9
PMCID: PMC3703642  PMID: 22914511
15.  Provider management strategies of abnormal test result alerts: a cognitive task analysis 
Objective
Electronic medical records (EMRs) facilitate abnormal test result communication through “alert” notifications. The aim was to evaluate how primary care providers (PCPs) manage alerts related to critical diagnostic test results on their EMR screens, and compare alert-management strategies of providers with high versus low rates of timely follow-up of results.
Design
28 PCPs from a large, tertiary care Veterans Affairs Medical Center (VAMC) were purposively sampled according to their rates of timely follow-up of alerts, determined in a previous study. Using techniques from cognitive task analysis, participants were interviewed about how and when they manage alerts, focusing on four alert-management features to filter, sort and reduce unnecessary alerts on their EMR screens.
Results
Provider knowledge of alert-management features ranged between 4% and 75%. Almost half (46%) of providers did not use any of these features, and none used more than two. Providers with higher versus lower rates of timely follow-up used the four features similarly, except one (customizing alert notifications). Providers with low rates of timely follow-up tended to manually scan the alert list and process alerts heuristically using their clinical judgment. Additionally, 46% of providers used at least one workaround strategy to manage alerts.
Conclusion
Considerable heterogeneity exists in provider use of alert-management strategies; specific strategies may be associated with lower rates of timely follow-up. Standardization of alert-management strategies including improving provider knowledge of appropriate tools in the EMR to manage alerts could reduce the lack of timely follow-up of abnormal diagnostic test results.
doi:10.1197/jamia.M3200
PMCID: PMC2995633  PMID: 20064805
medical records systems; computerized; task performance and analysis; diagnostic errors/classification; primary healthcare; software
16.  Fatal Dengue Hemorrhagic Fever in Adults: Emphasizing the Evolutionary Pre-fatal Clinical and Laboratory Manifestations 
Background
A better description of the clinical and laboratory manifestations of fatal patients with dengue hemorrhagic fever (DHF) is important in alerting clinicians of severe dengue and improving management.
Methods and Findings
Of 309 adults with DHF, 10 fatal patients and 299 survivors (controls) were retrospectively analyzed. Regarding causes of fatality, massive gastrointestinal (GI) bleeding was found in 4 patients, dengue shock syndrome (DSS) alone in 2; DSS/subarachnoid hemorrhage, Klebsiella pneumoniae meningitis/bacteremia, ventilator associated pneumonia, and massive GI bleeding/Enterococcus faecalis bacteremia each in one. Fatal patients were found to have significantly higher frequencies of early altered consciousness (≤24 h after hospitalization), hypothermia, GI bleeding/massive GI bleeding, DSS, concurrent bacteremia with/without shock, pulmonary edema, renal/hepatic failure, and subarachnoid hemorrhage. Among those experienced early altered consciousness, massive GI bleeding alone/with uremia/with E. faecalis bacteremia, and K. pneumoniae meningitis/bacteremia were each found in one patient. Significantly higher proportion of bandemia from initial (arrival) laboratory data in fatal patients as compared to controls, and higher proportion of pre-fatal leukocytosis and lower pre-fatal platelet count as compared to initial laboratory data of fatal patients were found. Massive GI bleeding (33.3%) and bacteremia (25%) were the major causes of pre-fatal leukocytosis in the deceased patients; 33.3% of the patients with pre-fatal profound thrombocytopenia (<20000/µL), and 50% of the patients with pre-fatal prothrombin time (PT) prolongation experienced massive GI bleeding.
Conclusions
Our report highlights causes of fatality other than DSS in patients with severe dengue, and suggested hypothermia, leukocytosis and bandemia may be warning signs of severe dengue. Clinicians should be alert to the potential development of massive GI bleeding, particularly in patients with early altered consciousness, profound thrombocytopenia, prolonged PT and/or leukocytosis. Antibiotic(s) should be empirically used for patients at risk for bacteremia until it is proven otherwise, especially in those with early altered consciousness and leukocytosis.
Author Summary
Fatality rate and causes of fatality in dengue-affected patients greatly varied from one reported series to another. A better understanding of the clinical and laboratory manifestations of fatal patients with dengue hemorrhagic fever (DHF) is important in alerting clinicians of severe dengue and improving management. In a retrospective analysis of 10 adults who died of and 299 survived (controls) DHF, dengue shock syndrome (DSS) alone was found in only 20% of dengue-related death, while intractable massive gastrointestinal (GI) bleeding was found in 40%, and DSS with concurrent subarachnoid hemorrhage, intractable massive GI bleeding with concurrent bacteremia, bacterial sepsis/meningitis, and sepsis due to ventilator associated pneumonia each were found in 10%. Early altered consciousness (developed ≤24 h after hospitalization), GI bleeding/massive GI bleeding and concurrent bacteremia were significantly found among the deceased patients. Our data suggest that hypothermia, leukocytosis and bandemia at hospital presentation may be warning signs of severe dengue. Clinicians should be alert to the potential development of massive GI bleeding, particularly in patients with early altered consciousness, profound thrombocytopenia, prothrombin time prolongation and/or leukocytosis. Antibiotic(s) should be empirically used for patients at risk for bacteremia until it is proven otherwise, especially in those with early altered consciousness and leukocytosis.
doi:10.1371/journal.pntd.0001532
PMCID: PMC3283557  PMID: 22363829
17.  Routine Eye Examinations for Persons 20-64 Years of Age 
Executive Summary
Objective
The objective of this analysis was to determine the strength of association between age, gender, ethnicity, family history of disease and refractive error and the risk of developing glaucoma or ARM?
Clinical Need
A routine eye exam serves a primary, secondary, and tertiary care role. In a primary care role, it allows contact with a doctor who can provide advice about eye care, which may reduce the incidence of eye disease and injury. In a secondary care role, it can via a case finding approach, diagnose persons with degenerative eye diseases such as glaucoma and or AMD, and lead to earlier treatment to slow the progression of the disease. Finally in a tertiary care role, it provides ongoing monitoring and treatment to those with diseases associated with vision loss.
Glaucoma is a progressive degenerative disease of the optic nerve, which causes gradual loss of peripheral (side) vision, and in advanced disease states loss of central vision. Blindness may results if glaucoma is not diagnosed and managed. The prevalence of primary open angle glaucoma (POAG) ranges from 1.1% to 3.0% in Western populations, and from 4.2% to 8.8% in populations of African descent. It is estimated up to 50% of people with glaucoma are aware that they have the disease. In Canada, glaucoma disease is the second leading cause of blindness in people aged 50 years and older. Tonometry, inspection of the optic disc and perimetry are used concurrently by physicians and optometrists to make the diagnosis of glaucoma. In general, the evidence shows that treating people with increased IOP only, increased IOP and clinical signs of early glaucoma or with normal-tension glaucoma can reduce the progression of disease.
Age-related maculopathy (ARM) is a degenerative disease of the macula, which is a part of the retina. Damage to the macula causes loss of central vision affecting the ability to read, recognize faces and to move about freely. ARM can be divided into an early- stage (early ARM) and a late-stage (AMD). AMD is the leading cause of blindness in developed countries. The prevalence of AMD increases with increasing age. It is estimated that 1% of people 55 years of age, 5% aged 75 to 84 years and 15% 80 years of age and older have AMD. ARM can be diagnosed during fundoscopy (ophthalmoscopy) which is a visual inspection of the retina by a physician or optometrist, or from a photograph of the retina. There is no cure or prevention for ARM. Likewise, there is currently no treatment to restore vision lost due to AMD. However, there are treatments to delay the progression of the disease and further loss of vision.
The Technology
A periodic oculo-visual assessment is defined “as an examination of the eye and vision system rendered primarily to determine if a patient has a simple refractive error (visual acuity assessment) including myopia, hypermetropia, presbyopia, anisometropia or astigmatism.” This service includes a history of the presenting complaint, past medical history, visual acuity examination, ocular mobility examination, slit lamp examination of the anterior segment, ophthalmoscopy, and tonometry (measurement of IOP) and is completed by either a physician or an optometrist.
Review Strategy
The Medical Advisory Secretariat conducted a computerized search of the literature in the following databases: OVID MEDLINE, MEDLINE, In-Process & Other Non-Indexed Citations, EMBASE, INAHTA and the Cochrane Library. The search was limited to English-language articles with human subjects, published from January 2000 to March 2006. In addition, a search was conducted for published guidelines, health technology assessments, and policy decisions. Bibliographies of references of relevant papers were searched for additional references that may have been missed in the computerized database search. Studies including participants 20 years and older, population-based prospective cohort studies, population-based cross-sectional studies when prospective cohort studies were unavailable or insufficient and studies determining and reporting the strength of association or risk- specific prevalence or incidence rates of either age, gender, ethnicity, refractive error or family history of disease and the risk of developing glaucoma or AMD were included in the review. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was used to summarize the overall quality of the body of evidence.
Summary of Findings
A total of 498 citations for the period January 2000 through February 2006 were retrieved and an additional 313 were identified when the search was expanded to include articles published between 1990 and 1999. An additional 6 articles were obtained from bibliographies of relevant articles. Of these, 36 articles were retrieved for further evaluation. Upon review, 1 meta-analysis and 15 population-based epidemiological studies were accepted for this review
Primary Open Angle Glaucoma
Age
Six cross-sectional studies and 1 prospective cohort study contributed data on the association between age and PAOG. From the data it can be concluded that the prevalence and 4-year incidence of POAG increases with increasing age. The odds of having POAG are statistically significantly greater for people 50 years of age and older relative to those 40 to 49 years of age. There is an estimated 7% per year incremental odds of having POAG in persons 40 years of age and older, and 10% per year in persons 49 years of age and older. POAG is undiagnosed in up to 50% of the population. The quality of the evidence is moderate.
Gender
Five cross-sectional studies evaluated the association between gender and POAG. Consistency in estimates is lacking among studies and because of this the association between gender and prevalent POAG is inconclusive. The quality of the evidence is very low.
Ethnicity
Only 1 cross-sectional study compared the prevalence rates of POAG between black and white participants. These data suggest that prevalent glaucoma is statistically significantly greater in a black population 50 years of age and older compared with a white population of similar age. There is an overall 4-fold increase in prevalent POAG in a black population compared with a white population. This increase may be due to a confounding variable not accounted for in the analysis. The quality of the evidence is low.
Refractive Error
Four cross-sectional studies assessed the association of myopia and POAG. These data suggest an association between myopia defined as a spherical equivalent of -1.00D or worse and prevalent POAG. However, there is inconsistency in results regarding the statistical significance of the association between myopia when defined as a spherical equivalent of -0.5D. The quality of the evidence is very low.
Family History of POAG
Three cross-sectional studies investigated the association between family history of glaucoma and prevalent POAG. These data suggest a 2.5 to 3.0 fold increase in the odds having POAG in persons with a family history (any first-degree relative) of POAG. The quality of the evidence is moderate.
Age-Related Maculopathy
Age
Four cohort studies evaluated the association between age and early ARM and AMD. After 55 years of age, the incidence of both early ARM and AMD increases with increasing age. Progression to AMD occurs in up to 12% of persons with early ARM. The quality of the evidence is low
Gender
Four cohort studies evaluated the association between gender and early ARM and AMD. Gender differences in incident early ARM and incident AMD are not supported from these data. The quality of the evidence is lows.
Ethnicity
One meta-analysis and 2 cross-sectional studies reported the ethnic-specific prevalence rates of ARM. The data suggests that the prevalence of early ARM is higher in a white population compared with a black population. The data suggest that the ethnic-specific differences in the prevalence of AMD remain inconclusive.
Refractive Error
Two cohort studies investigated the association between refractive error and the development of incident early ARM and AMD. The quality of the evidence is very low.
Family History
Two cross-sectional studies evaluated the association of family history and early ARM and AMD. Data from one study supports an association between a positive family history of AMD and having AMD. The results of the study indicate an almost 4-fold increase in the odds of any AMD in a person with a family history of AMD. The quality of the evidence, as based on the GRADE criteria is moderate.
Economic Analysis
The prevalence of glaucoma is estimated at 1 to 3% for a Caucasian population and 4.2 to 8.8% for a black population. The incidence of glaucoma is estimated at 0.5 to 2.5% per year in the literature. The percentage of people who go blind per year as a result of glaucoma is approximately 0.55%.
The total population of Ontarians aged 50 to 64 years is estimated at 2.6 million based on the April 2006 Ontario Ministry of Finance population estimates. The range of utilization for a major eye examination in 2006/07 for this age group is estimated at 567,690 to 669,125, were coverage for major eye exams extended to this age group. This would represent a net increase in utilization of approximately 440,116 to 541,551.
The percentage of Ontario population categorized as black and/or those with a family history of glaucoma was approximately 20%. Therefore, the estimated range of utilization for a major eye examination in 2006/07 for this sub-population is estimated at 113,538 - 138,727 (20% of the estimated range of utilization in total population of 50-64 year olds in Ontario), were coverage for major eye exams extended to this sub-group. This would represent a net increase in utilization of approximately 88,023 to 108,310 within this sub-group.
Costs
The total cost of a major eye examination by a physician is $42.15, as per the 2006 Schedule of Benefits for Physician Services.(1) The total difference between the treatments of early-stage versus late-stage glaucoma was estimated at $167. The total cost per recipient was estimated at $891/person.
Current Ontario Policy
As of November 1, 2004 persons between 20 years and 64 years of age are eligible for an insured eye examination once every year if they have any of the following medical conditions: diabetes mellitus type 1 or 2, glaucoma, cataract(s), retinal disease, amblyopia, visual field defects, corneal disease, or strabismus. Persons between 20 to 64 years of age who do not have diabetes mellitus, glaucoma, cataract(s), retinal disease, amblyopia, visual field defects, corneal disease, or strabismus may be eligible for an annual eye examination if they have a valid “request for major eye examination” form completed by a physician (other than that who completed the eye exam) or a nurse practitioner working in a collaborative practice. Persons 20-64 years of age who are in receipt of social assistance and who do not have one of the 8 medical conditions listed above are eligible to receive an eye exam once every 2 years as a non-OHIP government funded service. Persons 19 years of age or younger and 65 years of age or older may receive an insured eye exam once every year.
Considerations for Policy Development
As of July 17, 2006 there were 1,402 practicing optometrists in Ontario. As of December 31, 2005 there were 404 practicing ophthalmologists in Ontario. It is unknown how many third party payers now cover routine eye exams for person between the ages of 20 and 64 years of age in Ontario.
PMCID: PMC3379534  PMID: 23074485
18.  System Related Interventions to Reduce Diagnostic Error: A Narrative Review 
BMJ quality & safety  2011;21(2):160-170.
Background
Diagnostic errors (missed, delayed, or wrong diagnosis) have gained recent attention and are associated with significant preventable morbidity and mortality. We reviewed the recent literature to identify interventions that have been, or could be, implemented to address systems-related factors that contribute directly to diagnostic error.
Methods
We conducted a comprehensive search using multiple search strategies. We first identified candidate articles in English between 2000 and 2009 from a PubMed search that exclusively evaluated for articles related to diagnostic error or delay. We then sought additional papers from references in the initial dataset, searches of additional databases, and subject matter experts. Articles were included if they formally evaluated an intervention to prevent or reduce diagnostic error; however, we also included papers if interventions were suggested and not tested in order to inform the state-of-the science on the topic. We categorized interventions according to the step in the diagnostic process they targeted: patient-provider encounter, performance and interpretation of diagnostic tests, follow-up and tracking of diagnostic information, subspecialty and referral-related; and patient-specific.
Results
We identified 43 articles for full review, of which 6 reported tested interventions and 37 contained suggestions for possible interventions. Empirical studies, though somewhat positive, were non-experimental or quasi-experimental and included a small number of clinicians or health care sites. Outcome measures in general were underdeveloped and varied markedly between studies, depending on the setting or step in the diagnostic process involved.
Conclusions
Despite a number of suggested interventions in the literature, few empirical studies have tested interventions to reduce diagnostic error in the last decade. Advancing the science of diagnostic error prevention will require more robust study designs and rigorous definitions of diagnostic processes and outcomes to measure intervention effects.
doi:10.1136/bmjqs-2011-000150
PMCID: PMC3677060  PMID: 22129930
19.  Missing Clinical Information in NHS hospital outpatient clinics: prevalence, causes and effects on patient care 
Background
In Britain over 39,000 reports were received by the National Patient Safety Agency relating to failures in documentation in 2007 and the UK Health Services Journal estimated in 2008 that over a million hospital outpatient visits each year might take place without the full record available. Despite these high numbers, the impact of missing clinical information has not been investigated for hospital outpatients in the UK.
Studies in primary care in the USA have found 13.6% of patient consultations have missing clinical information, with this adversely affecting care in about half of cases, and in Australia 1.8% of medical errors were found to be due to the unavailability of clinical information.
Our objectives were to assess the frequency, nature and potential impact on patient care of missing clinical information in NHS hospital outpatients and to assess the principal causes. This is the first study to present such figures for the UK and the first to look at how clinicians respond, including the associated impact on patient care.
Methods
Prospective descriptive study of missing information reported by surgeons, supplemented by interviews on the causes.
Data were collected by surgeons in general, gastrointestinal, colorectal and vascular surgical clinics in three teaching hospitals across the UK for over a thousand outpatient appointments. Fifteen interviews were conducted with those involved in collating clinical information for these clinics.
The study had ethics approval (Hammersmith and Queen Charlotte's & Chelsea Research Ethics Committee), reference number (09/H0707/27). Participants involved in the interviews signed a consent form and were offered the opportunity to review and agree the transcript of their interview before analysis. No patients were involved in this research.
Results
In 15% of outpatient consultations key items of clinical information were missing. Of these patients, 32% experienced a delay or disruption to their care and 20% had a risk of harm. In over half of cases the doctor relied on the patient for the information, making a clinical decision despite the information being missing in 20% of cases. Hospital mergers, temporary staff and non-integrated IT systems were contributing factors.
Conclusions
If these findings are replicated across the NHS then almost 10 million outpatients are seen each year without key clinical information, creating over a million unnecessary appointments, and putting nearly 2 million patients at risk of harm. There is a need for a systematic, regular audit of the prevalence of missing clinical information. Only then will we know the impact on clinical decision making and patient care of new technology, service reorganisations and, crucially given the present financial climate, temporary or reduced staffing levels. Further research is needed to assess the relationship between missing clinical information and diagnostic errors; to examine the issue in primary care; and to consider the patients perspective.
doi:10.1186/1472-6963-11-114
PMCID: PMC3118108  PMID: 21605359
20.  Are We Heeding the Warning Signs? Examining Providers’ Overrides of Computerized Drug-Drug Interaction Alerts in Primary Care 
PLoS ONE  2013;8(12):e85071.
Background
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.
Objective
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.
Design
A cross-sectional, observational study of DDI alerts generated over a three-year period between January 1st, 2009, and December 31st, 2011.
Setting
Primary care practices affiliated with two Harvard teaching hospitals. The DDI alerts were screened to minimize the number of clinically unimportant warnings.
Participants
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).
Results
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.
doi:10.1371/journal.pone.0085071
PMCID: PMC3873469  PMID: 24386447
21.  The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations 
BMJ Quality & Safety  2014;23(9):727-731.
Background
The frequency of outpatient diagnostic errors is challenging to determine due to varying error definitions and the need to review data across multiple providers and care settings over time. We estimated the frequency of diagnostic errors in the US adult population by synthesising data from three previous studies of clinic-based populations that used conceptually similar definitions of diagnostic error.
Methods
Data sources included two previous studies that used electronic triggers, or algorithms, to detect unusual patterns of return visits after an initial primary care visit or lack of follow-up of abnormal clinical findings related to colorectal cancer, both suggestive of diagnostic errors. A third study examined consecutive cases of lung cancer. In all three studies, diagnostic errors were confirmed through chart review and defined as missed opportunities to make a timely or correct diagnosis based on available evidence. We extrapolated the frequency of diagnostic error obtained from our studies to the US adult population, using the primary care study to estimate rates of diagnostic error for acute conditions (and exacerbations of existing conditions) and the two cancer studies to conservatively estimate rates of missed diagnosis of colorectal and lung cancer (as proxies for other serious chronic conditions).
Results
Combining estimates from the three studies yielded a rate of outpatient diagnostic errors of 5.08%, or approximately 12 million US adults every year. Based upon previous work, we estimate that about half of these errors could potentially be harmful.
Conclusions
Our population-based estimate suggests that diagnostic errors affect at least 1 in 20 US adults. This foundational evidence should encourage policymakers, healthcare organisations and researchers to start measuring and reducing diagnostic errors.
doi:10.1136/bmjqs-2013-002627
PMCID: PMC4145460  PMID: 24742777
Trigger Tools; Diagnostic Errors; Patient Safety; Chart Review Methodologies; Medical Error, Measurement/Epidemiology
22.  ‘Shouting from the roof tops’: a qualitative study of how children with leukaemia are diagnosed in primary care 
BMJ Open  2014;4(2):e004640.
Objectives
To investigate the prehospital presentation of paediatric leukaemia and identify the disease and non-disease related factors which facilitate or impede diagnosis.
Design
Thematic analysis of qualitative semistructured interviews.
Setting
One tertiary referral centre in Southern England.
Participants
21 parents and 9 general practitioners (GPs) of 18 children (<18-year-old) with a new diagnosis of acute leukaemia.
Results
The majority of children were first seen by GPs before the characteristic signs and symptoms of leukaemia had developed. In their absence, behavioural cues such as the child becoming apathetic or ‘not themselves’ often triggered parents to seek medical help. Most GPs were unclear about the nature and severity of the child's presentation: then, safety netting, thorough history-taking and examination, and reliance on contextual information about the parents or from prior hospital paediatrics experience were used to manage diagnostic uncertainty. The nature of the doctor–parent relationship helped and hindered the diagnostic pathway. GPs’ prior perceptions of parents as being ‘sensible’ or ‘worriers’ influenced how gravely they treated parental concerns, with ‘worriers’ being taken less seriously. Some parents believed GPs failed to listen to their anxieties and discounted their expert knowledge of their child. Specific delay factors included lack of continuity of GP; some GPs’ reluctance to take blood from children; and some parents feeling unable to voice effectively their concerns.
Conclusions
The presentation of paediatric leukaemia in primary care differs from that described in many hospital studies, with greater diversity and intermittency of symptoms, and the frequent absence of ‘red flags’ of serious illness. A wide range of non-disease related factors potentially delay the diagnosis of paediatric leukaemia, including tensions in the doctor–patient relationship and the doctors’ cognitive biases. The identification and attempted modification of these factors may minimise diagnostic delay more successfully than raising awareness of ‘red flags’ of disease.
doi:10.1136/bmjopen-2013-004640
PMCID: PMC3931998  PMID: 24549167
Qualitative Research
23.  Stopping the error cascade: a report on ameliorators from the ASIPS collaborative 
Objective
To present a novel examination of how error cascades are stopped (ameliorated) before they affect patients.
Design
Qualitative analysis of reported errors in primary care.
Setting
Over a three‐year period, clinicians and staff in two practice‐based research networks voluntarily reported medical errors to a primary care patient safety reporting system, Applied Strategies for Improving Patient Safety (ASIPS). The authors found a number of reports where the error was corrected before it had an adverse impact on the patient.
Results
Of 754 codeable reported events, 60 were classified as ameliorated events. In these events, a participant stopped the progression of the event before it reached or affected the patient. Ameliorators included doctors, nurses, pharmacists, diagnostic laboratories and office staff. Additionally, patients or family members may be ameliorators by recognising the error and taking action. Ameliorating an event after an initial error requires an opportunity to catch the error by systems, chance or attentiveness. Correcting the error before it affects the patient requires action either directed by protocols and systems or by vigilance, power to change course and perseverance on the part of the ameliorator.
Conclusion
Despite numerous individual and systematic methods to prevent errors, a system to prevent all potential errors is not feasible. However, a more pervasive culture of safety that builds on simple acts in addition to more costly and complex electronic systems may improve patient outcomes. Medical staff and patients who are encouraged to be vigilant, ask questions and seek solutions may correct otherwise inevitable wrongs.
doi:10.1136/qshc.2005.017269
PMCID: PMC2464918  PMID: 17301195
24.  “I Can't Find Anything Wrong: It Must Be a Pulmonary Embolism”: Diagnosing Suspected Pulmonary Embolism in Primary Care, a Qualitative Study 
PLoS ONE  2014;9(5):e98112.
Background
Before using any prediction rule oriented towards pulmonary embolism (PE), family physicians (FPs) should have some suspicion of this diagnosis. The diagnostic reasoning process leading to the suspicion of PE is not well described in primary care.
Objective
to explore the diagnostic reasoning of FPs when pulmonary embolism is suspected.
Method
Semi-structured qualitative interviews with 28 FPs. The regional hospital supplied data of all their cases of pulmonary embolism from June to November 2011. The patient's FP was identified where he/she had been the physician who had sent the patient to the emergency unit. The first consecutive 14 FPs who agreed to participate made up the first group. A second group was chosen using a purposeful sampling method. The topic guide focused on the circumstances leading to the suspicion of PE. A thematic analysis was performed, by three researchers, using a grounded theory coding paradigm.
Results
In the FPs' experience, the suspicion of pulmonary embolism arose out of four considerations: the absence of indicative clinical signs for diagnoses other than PE, a sudden change in the condition of the patient, a gut feeling that something was seriously wrong and an earlier failure to diagnose PE. The FPs interviewed did not use rules in their diagnostic process.
Conclusion
This study illustrated the diagnostic role of gut feelings in the specific context of suspected pulmonary embolism in primary care. The FPs used the sense of alarm as a tool to prevent the diagnostic error of missing a PE. The diagnostic accuracy of gut feelings has yet to be evaluated.
doi:10.1371/journal.pone.0098112
PMCID: PMC4026480  PMID: 24840333
25.  Implementation of a High-Alert Medication Program 
The Permanente Journal  2008;12(2):15-22.
Introduction: Greater than 500,000 doses of high-alert medications are administered throughout the Kaiser Permanente Northern California (KPNC) Program on an annual basis. High-alert medications (HAM) carry a higher risk of harm than other medications and errors in the administration of HAM can have catastrophic clinical outcomes. The purpose of this project is to ensure safe medication practices and to eliminate medication errors that cause harm to our patients.
The Program: KPNC leadership, physicians, nurses, pharmacists, quality leaders, and labor unions worked with regional and local medication safety committees to: 1) standardize high-alert medication-handling practices; 2) enhance education programs related to medication practices, embedding these into annual core competencies of all staff who handle high-alert medications; 3) develop monitoring functions at both the regional and local levels to ensure sustainability and ongoing systems improvements. Begun in December 2005, this program covers the delivery of high-alert medications across the continuum of care and affects all patients receiving HAM.
Measures: The initial phase of the monitoring process was put in place to measure compliance with implementation. Over the first few months of the program the 90% minimal threshold was surpassed with regional overall compliance of 95%. Following this initial process, the Regional Medication Safety Committee developed monitoring tools. Department managers carry out these concurrent observational audits at the medical centers with oversight by the Assistant Administrators for Quality and Service. These audits are designed to measure whether or not all medications on the HAM list are handled specifically to policy requirements, eg, independent double-checks, HAM stickers, etc. Audit specifications are provided for each audit tool. Medical Center audit results from the third quarter of 2006 through the third quarter of 2007 have shown a regional aggregate of 97.7% compliance. As the high percentages of compliance have held constant over time, more actionable metrics are being put in place for 2008.
To determine whether or not the program is reducing HAM errors, data from the regional Quality and Risk database (MIDAS) related to all high-alert medication errors was reviewed. Two interventions were of note: in July of 2005, there was a renewed effort to educate leaders, managers, physicians, and staff on responsible reporting in a “just culture” and the introduction of the new Responsible Reporting Form. An increase in reporting was noted at this time. In December 2005, the HAM program was introduced. There is a statistically significant drop in errors reported for 23 consecutive months following this program. These findings were similar for all phases of the delivery process. A powerful indicator of improvement is the average days between major injury and death. As of November 30, 2007, it has been 232 days since the last significant negative event was reported due to a HAM.
Conclusion: This program has been implemented in all of the KPNC Medical Centers and is in the process of being implemented in all KP regions. This spread has been endorsed by the Medical Directors Quality Committee and by the KP Boards of Directors. The Interregional Medication Safety Committee is overseeing the spread process. A toolkit containing all of the required tools plus additional materials and information has been developed and made available throughout KP. The program is the recipient of the 2007 Lawrence Patient Safety Award.
PMCID: PMC3042285  PMID: 21364807

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