Ugochi Nwulu

Large scale organisational intervention to improve patient safety in four UK hospitals: mixed method evaluation

Objectives To conduct an independent evaluation of the first phase of the Health Foundation’s Safer Patients Initiative (SPI), and to identify the net additional effect of SPI and any differences in changes in participating and non-participating NHS hospitals. Design Mixed method evaluation involving five substudies, before and after design. Setting NHS hospitals in the United Kingdom. Participants Four hospitals (one in each country in the UK) participating in the first phase of the SPI (SPI1); 18 control hospitals. Intervention The SPI1 was a compound (multi-component) organisational intervention delivered over 18 months that focused on improving the reliability of specific frontline care processes in designated clinical specialties and promoting organisational and cultural change. Results Senior staff members were knowledgeable and enthusiastic about SPI1. There was a small (0.08 points on a 5 point scale) but significant (P<0.01) effect in favour of the SPI1 hospitals in one of 11 dimensions of the staff questionnaire (organisational climate). Qualitative evidence showed only modest penetration of SPI1 at medical ward level. Although SPI1 was designed to engage staff from the bottom up, it did not usually feel like this to those working on the wards, and questions about legitimacy of some aspects of SPI1 were raised. Of the five components to identify patients at risk of deterioration—monitoring of vital signs (14 items); routine tests (three items); evidence based standards specific to certain diseases (three items); prescribing errors (multiple items from the British National Formulary); and medical history taking (11 items)—there was little net difference between control and SPI1 hospitals, except in relation to quality of monitoring of acute medical patients, which improved on average over time across all hospitals. Recording of respiratory rate increased to a greater degree in SPI1 than in control hospitals; in the second six hours after admission recording increased from 40% (93) to 69% (165) in control hospitals and from 37% (141) to 78% (296) in SPI1 hospitals (odds ratio for “difference in difference” 2.1, 99% confidence interval 1.0 to 4.3; P=0.008). Use of a formal scoring system for patients with pneumonia also increased over time (from 2% (102) to 23% (111) in control hospitals and from 2% (170) to 9% (189) in SPI1 hospitals), which favoured controls and was not significant (0.3, 0.02 to 3.4; P=0.173). There were no improvements in the proportion of prescription errors and no effects that could be attributed to SPI1 in non-targeted generic areas (such as enhanced safety culture). On some measures, the lack of effect could be because compliance was already high at baseline (such as use of steroids in over 85% of cases where indicated), but even when there was more room for improvement (such as in quality of medical history taking), there was no significant additional net effect of SPI1. There were no changes over time or between control and SPI1 hospitals in errors or rates of adverse events in patients in medical wards. Mortality increased from 11% (27) to 16% (39) among controls and decreased from 17% (63) to 13% (49) among SPI1 hospitals, but the risk adjusted difference was not significant (0.5, 0.2 to 1.4; P=0.085). Poor care was a contributing factor in four of the 178 deaths identified by review of case notes. The survey of patients showed no significant differences apart from an increase in perception of cleanliness in favour of SPI1 hospitals. Conclusions The introduction of SPI1 was associated with improvements in one of the types of clinical process studied (monitoring of vital signs) and one measure of staff perceptions of organisational climate. There was no additional effect of SPI1 on other targeted issues nor on other measures of generic organisational strengthening.

Multiple component patient safety intervention in English hospitals: controlled evaluation of second phase.

Objective To independently evaluate the impact of the second phase of the Health Foundation’s Safer Patients Initiative (SPI2) on a range of patient safety measures. Design A controlled before and after design. Five substudies: survey of staff attitudes; review of case notes from high risk (respiratory) patients in medical wards; review of case notes from surgical patients; indirect evaluation of hand hygiene by measuring hospital use of handwashing materials; measurement of outcomes (adverse events, mortality among high risk patients admitted to medical wards, patients’ satisfaction, mortality in intensive care, rates of hospital acquired infection). Setting NHS hospitals in England. Participants Nine hospitals participating in SPI2 and nine matched control hospitals. Intervention The SPI2 intervention was similar to the SPI1, with somewhat modified goals, a slightly longer intervention period, and a smaller budget per hospital. Results One of the scores (organisational climate) showed a significant (P=0.009) difference in rate of change over time, which favoured the control hospitals, though the difference was only 0.07 points on a five point scale. Results of the explicit case note reviews of high risk medical patients showed that certain practices improved over time in both control and SPI2 hospitals (and none deteriorated), but there were no significant differences between control and SPI2 hospitals. Monitoring of vital signs improved across control and SPI2 sites. This temporal effect was significant for monitoring the respiratory rate at both the six hour (adjusted odds ratio 2.1, 99% confidence interval 1.0 to 4.3; P=0.010) and 12 hour (2.4, 1.1 to 5.0; P=0.002) periods after admission. There was no significant effect of SPI for any of the measures of vital signs. Use of a recommended system for scoring the severity of pneumonia improved from 1.9% (1/52) to 21.4% (12/56) of control and from 2.0% (1/50) to 41.7% (25/60) of SPI2 patients. This temporal change was significant (7.3, 1.4 to 37.7; P=0.002), but the difference in difference was not significant (2.1, 0.4 to 11.1; P=0.236). There were no notable or significant changes in the pattern of prescribing errors, either over time or between control and SPI2 hospitals. Two items of medical history taking (exercise tolerance and occupation) showed significant improvement over time, across both control and SPI2 hospitals, but no additional SPI2 effect. The holistic review showed no significant changes in error rates either over time or between control and SPI2 hospitals. The explicit case note review of perioperative care showed that adherence rates for two of the four perioperative standards targeted by SPI2 were already good at baseline, exceeding 94% for antibiotic prophylaxis and 98% for deep vein thrombosis prophylaxis. Intraoperative monitoring of temperature improved over time in both groups, but this was not significant (1.8, 0.4 to 7.6; P=0.279), and there were no additional effects of SPI2. A dramatic rise in consumption of soap and alcohol hand rub was similar in control and SPI2 hospitals (P=0.760 and P=0.889, respectively), as was the corresponding decrease in rates of Clostridium difficile and meticillin resistant Staphylococcus aureus infection (P=0.652 and P=0.693, respectively). Mortality rates of medical patients included in the case note reviews in control hospitals increased from 17.3% (42/243) to 21.4% (24/112), while in SPI2 hospitals they fell from 10.3% (24/233) to 6.1% (7/114) (P=0.043). Fewer than 8% of deaths were classed as avoidable; changes in proportions could not explain the divergence of overall death rates between control and SPI2 hospitals. There was no significant difference in the rate of change in mortality in intensive care. Patients’ satisfaction improved in both control and SPI2 hospitals on all dimensions, but again there were no significant changes between the two groups of hospitals. Conclusions Many aspects of care are already good or improving across the NHS in England, suggesting considerable improvements in quality across the board. These improvements are probably due to contemporaneous policy activities relating to patient safety, including those with features similar to the SPI, and the emergence of professional consensus on some clinical processes. This phenomenon might have attenuated the incremental effect of the SPI, making it difficult to detect. Alternatively, the full impact of the SPI might be observable only in the longer term. The conclusion of this study could have been different if concurrent controls had not been used.

Developing consensus on hospital prescribing indicators of potential harms amenable to decision support

AIMS To develop a list of prescribing indicators specific for the hospital setting that would facilitate the prospective collection of high-severity and/or high-frequency prescribing errors, which are also amenable to electronic clinical decision support. METHODS A two-stage consensus technique (electronic Delphi) was carried out with 20 experts across England. Participants were asked to score prescribing errors using a five-point Likert scale for their likelihood of occurrence and the severity of the most likely outcome. These were combined to produce risk scores, from which median scores were calculated for each indicator across the participants in the study. The degree of consensus between the participants was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or more was achieved. RESULTS A total of 80 prescribing errors were identified by consensus as being high or extreme risk. The most common drug classes named within the indicators were antibiotics (n = 13), antidepressants (n = 8), nonsteroidal anti-inflammatory drugs (n = 6) and opioid analgesics (n = 6). The most frequent error type identified as high or extreme risk were those classified as clinical contraindications (n = 29 of 80). CONCLUSIONS Eighty high-risk prescribing errors in the hospital setting have been identified by an expert panel. These indicators can serve as a standardized, validated tool for the collection of prescribing data in both paper-based and electronic prescribing processes. This can assess the impact of safety improvement initiatives, such as the implementation of electronic clinical decision support.

Electronic risk assessment for venous thromboembolism: investigating physicians' rationale for bypassing clinical decision support recommendations

Objective The underutilisation of venous thromboembolism (VTE) prophylaxis is still a problem in the UK despite the emergence of national guidelines and incentives to increase the number of patients undergoing VTE risk assessments. Our objective was to examine the reasons doctors gave for not prescribing enoxaparin when recommended by an electronic VTE risk assessment alert. Design We used a qualitative research design to conduct a thematic analysis of free text entered into an electronic prescribing system. Setting The study took place in a large University teaching hospital, which has a locally developed electronic prescribing system known as PICS (Prescribing, Information and Communication System). Participants We extracted prescription data from all inpatient admissions over a 7-month period in 2012 using the audit database of PICS. Intervention The completion of the VTE risk assessment form introduced into the hospital-wide electronic prescribing and health records system is mandatory. Where doctors do not prescribe VTE prophylaxis when recommended, they are asked to provide a reason for this decision. The free-text field was introduced in May 2012. Primary and secondary outcome measures Free-text reasons for not prescribing enoxaparin when recommended were thematically coded. Results A total of 1136 free-text responses from 259 doctors were collected in the time period and 1206 separate reasons were analysed and coded. 389 reasons (32.3%) for not prescribing enoxaparin were coded as being due to ‘clinical judgment’; in 288 (23.9%) of the responses, doctors were going to reassess the patient or prescribe enoxaparin; and in 245 responses (20.3%), the system was seen to have produced an inappropriate alert. Conclusions In order to increase specificity of warnings and avoid users developing alert fatigue, it is essential that an evaluation of user responses and/or end user feedback as to the appropriateness and timing of alerts is obtained.

Decision support for sensible dosing in electronic prescribing systems

What is known and Objective:  The sensible dosing of medicines can ensure that patients receive neither excessive doses leading to toxicity nor inappropriately low doses leading to undertreatment. Computerized prescribing systems with embedded decision support can check doses during prescription order entry and display alerts when the prescribed doses are out of range. We have been unable to identify any scheme for the systematic addition of dosing information to CPOE systems.Summary What is known and Objective:  The sensible dosing of medicines can ensure that patients receive neither excessive doses leading to toxicity nor inappropriately low doses leading to undertreatment. Computerized prescribing systems with embedded decision support can check doses during prescription order entry and display alerts when the prescribed doses are out of range. We have been unable to identify any scheme for the systematic addition of dosing information to CPOE systems. We used pharmacological data to design an algorithm for dose range checking that we tested on a subset of medicines in an electronic prescribing system to ensure that the rules could be implemented in practice. Methods:  We drafted an initial algorithm based on pharmacological principles, tested it on a subset of frequently prescribed drugs in an electronic prescribing system and then refined it. We considered which clinical decision support functions systems would require to be maximally effective. Results and Discussion:  The final algorithm contained eleven broad factors. We tested it on 30 drug-route-form combinations, and it accommodated the information for all of these combinations. We also identified a variety of system functions that would be required for comprehensive dosing decision support. What is new and Conclusion:  The dose range checking algorithm that we have derived from first principles will allow the clinical workflow and warnings to be constructed more effectively within systems to enhance patient safety. This will form a basis for the development of optimal schemes for adding decision support to prescribing systems.

Adoption of an electronic observation chart with an integrated early warning scoring system on pilot wards: a descriptive report.

The charting of physiological variables in hospital inpatients allows for recognition and treatment of deteriorating patients. The use of electronic records to capture patients’ vital signs is still in its infancy in the United Kingdom. The main objective of this article was to describe the adoption of an electronic observation charting function integrated into an established bedside e-prescribing record system on acute wards in a large English university hospital. This new function also has the capability of contacting Critical Care Outreach and clinical staff when patients deteriorate. Data captured over a 4-month period from the pilot wards showed that 80% of observation sets were completed sufficiently to produce early warning scores over the time period. A daily average of 419 Standardized Early Warning Score produced 74 alerts to clinical staff, and two critical alarms per day were e-mailed to the Outreach team. The wards showed different levels of completeness of observations (from 69% to 92%). Although a good overall rate of completeness of physiological data was found, traditional gaps in observation recording documented in the literature (eg, recording of respiratory rate) were still apparent. This system can be used for audit for targeted staff education and to evaluate the Critical Care Outreach service.

Variation in cost of newly qualified doctors’ prescriptions: a review of data from a hospital electronic prescribing system

Purpose of the study To investigate the variation in the net ingredient cost (NIC) of the medications most commonly prescribed by Foundation Year 1 (F1) doctors in a teaching hospital and to compare the effects of working in different specialties and rotations on this cost. Design of the study Retrospective review of prescription data from 5 August 2010 to 3 August 2011 extracted from an electronic prescribing system. Results The F1 doctors generated 81 316 prescriptions with an estimated total cost of £579 398. The mean NIC per doctor was £7334 (SE=£430). Prescribing costs varied significantly across clinical departments and between drug classes considered in the analysis. Specifically, prescribing in the infection and respiratory drug categories and within the trauma and orthopaedics department was associated with higher prescribing costs. Significant variability was also attributable to the prescribing doctor (p<0.001) with average prescription costs ranging from 72.2% lower to 193.8% higher than the median doctor. Conclusions There is considerable variation in the total costs of medications prescribed by F1 doctors, even after considering a range of prescription factors. This variation may suggest that some doctors are prescribing uneconomically relative to the rest of the cohort. Knowledge of which clinical areas and drug classes have higher NICs may allow an alternative focus for medicine management teams and postgraduate education.

bernard Nchewa Nwulu

Bernard Nchewa Nwulu was born in Umuode Nsulu, Nigeria, and won a scholarship to study medicine at the University of Lovanium in Zaire. After qualifying, he stayed in Zaire, worked as a district medical officer for a Belgian railway company, and was the sole general practitioner for more than 150 000 people in the region. He made a pragmatic choice to specialise in psychiatry when he returned to Nigeria three years later. He moved to Scotland in 1978 to study at the University of Edinburgh and wrote …