A systematic review to investigate the effect of digital antimicrobial stewardship tools on antimicrobial usage, length of stay, mortality and cost

Introduction

Antimicrobial drug resistance has been recognised by the World Health Organisation as ‘One of the biggest threats to global health today’.1 As the use of digital systems in the NHS increases, there is huge potential to use systems such as electronic prescribing and clinical decision support as part of Antimicrobial Stewardship Programmes (ASPs) i.e., initiatives to change prescribing practices to promote and monitor use of antimicrobials and preserve their future effectiveness. However, there is a lack of research that has investigated the impact of digital tools as part of ASPs.

Aim

We aimed to review the literature available on the use of digital antimicrobial stewardship tools on individual outcomes such as antimicrobial usage, length of stay, mortality and cost.

Methods

A systematic search was performed across three databases (Embase, MEDLINE and CINAHL) using MESH terms and key words relating to antimicrobial stewardship, hospitals, length of stay (LOS), clinical outcomes, cost and mortality. Duplicates were removed and articles screened at the title, abstract and full text stage by two authors (NT and RK) according to our inclusion and exclusion criteria. We included primary research articles that: had implemented an ASPs in an adult hospital setting for at least 6 months, reported antimicrobial usage as defined daily dose per 1000 patient days (DDD/1000) and at least one of the following outcomes: LOS, mortality or cost and discussed an ASP that included a digital component. Risk of bias assessment was performed using the Newcastle-Ottawa scale. We calculated the percentage change to determine the impact of digital ASPs across all outcomes using the formula (After - Before)/Before x 100 = % Change.

Before=pre-implementation results; after= results post-implementation

Results

We identified 3997 papers across all databases, and included 14 full texts that explored the impact of ASPs including a digital component (Figure 1). Of these, 14 papers reported the DDD/1000, 7 on mortality, 8 on LoS and 6 reported on cost. All studies evaluating DDD/1000 reported a decrease in antimicrobial usage ranging from -8.42% to -61.30%. Reductions in mortality (0 to -79%), LoS (25 to -27%) and costs (-8.42% to -69.19%) were also found. All ASPs utilised a digital component alongside a range of other interventions, such as the creation of formularies, guidelines and education emphasising the importance of using a combined approach in antimicrobial stewardship. Different interventions were found to have their own advantages, for example, education was key to sustainability and feedback was essential to improve prescribing practices. Users of the digital tools found that the tools were generally simple and user friendly, which facilitated their acceptance.

Conclusion

Our found that ASPs including a digital component were associated with reductions in antimicrobial usage, mortality, length of stay and cost. The positive effects were seen when such tools were combined with other approaches such as education and feedback approaches. We were unable to perform a meta-analysis due to the absence of confidence intervals and odds ratios in many of the included studies. Further research is needed to evaluate the cost-benefit associated with digital ASPs and whether sharing ASPs across multiple sites could reduce the maintenance burden for individual organisations.

References

1. World Health Organisation (2020), Antibiotic Resistance Factsheet, https://www.who.int/news-room/fact-sheets/detail/antibiotic-resistance [accessed on 18th October 2020]

Predicting infection and sepsis; what predictors have been used to train machine learning algorithms? A systematic review

Introduction

Sepsis is a life-threatening condition that is associated with increased mortality. Artificial intelligence tools can inform clinical decision making by flagging patients who may be at risk of developing infection and subsequent sepsis and assist clinicians with their care management.

Aim

To identify the optimal set of predictors used to train machine learning algorithms to predict the likelihood of an infection and subsequent sepsis and inform clinical decision making.

Methods

This systematic review was registered in PROSPERO database (CRD42020158685). We searched 3 large databases: Medline, Cumulative Index of Nursing and Allied Health Literature, and Embase, using appropriate search terms. We included quantitative primary research studies that focused on sepsis prediction associated with bacterial infection in adult population (>18 years) in all care settings, which included data on predictors to develop machine learning algorithms. The timeframe of the search was 1st January 2000 till the 25th November 2019. Data extraction was performed using a data extraction sheet, and a narrative synthesis of eligible studies was undertaken. Narrative analysis was used to arrange the data into key areas, and compare and contrast between the content of included studies. Quality assessment was performed using Newcastle-Ottawa Quality Assessment scale, which was used to evaluate the quality of non-randomized studies. Bias was not assessed due to the non-randomised nature of the included studies.

Results

Fifteen articles met our inclusion criteria (Figure 1). We identified 194 predictors that were used to train machine learning algorithms to predict infection and subsequent sepsis, with 13 predictors used on average across all included studies. The most significant predictors included age, gender, smoking, alcohol intake, heart rate, blood pressure, lactate level, cardiovascular disease, endocrine disease, cancer, chronic kidney disease (eGFR<60ml/min), white blood cell count, liver dysfunction, surgical approach (open or minimally invasive), and pre-operative haematocrit < 30%. These predictors were used for the development of all the algorithms in the fifteen articles. All included studies used artificial intelligence techniques to predict the likelihood of sepsis, with average sensitivity 77.5±19.27, and average specificity 69.45±21.25.

Conclusion

The type of predictors used were found to influence the predictive power and predictive timeframe of the developed machine learning algorithm. Two strengths of our review were that we included studies published since the first definition of sepsis was published in 2001, and identified factors that can improve the predictive ability of algorithms. However, we note that the included studies had some limitations, with three studies not validating the models that they developed, and many tools limited by either their reduced specificity or sensitivity or both. This work has important implications for practice, as predicting the likelihood of sepsis can help inform the management of patients and concentrate finite resources to those patients who are most at risk. Producing a set of predictors can also guide future studies in developing more sensitive and specific algorithms with increased predictive time window to allow for preventive clinical measures.

The Impact of a Bedside Medication Scanning Device on Administration Errors in the Hospital Setting: A Prospective Observational Study

Introduction

The medication administration process is complex and influenced by interruptions, multi-tasking and responding to patient’s needs and is consequently prone to errors.1 Over half (54.4%) of the 237 million medication errors estimated to have occurred in England each year were found to have taken place at the administration stage and 7.6% were associated with moderate or severe harm. The implementation of a Closed Loop Medication Administration solution aims to reduce medication administration errors and prevent patient harm.

Aim

We conducted the first evaluation to assess the impact of a novel optical medication scanning device, MedEye, on the rate of medication administration errors in solid oral dosage forms.

Methods

We performed a before and after study on one ward at a tertiary-care teaching hospital that used a commercial electronic prescribing and medication administration system and was implementing MedEye (a bedside tool for stopping and preventing medication administration errors). Pre-MedEye data collection occurred between Aug-Nov 2019 and post-MedEye data collection occurred between Feb-Mar 2020. We conducted direct observations of nursing drug administration rounds before and after the MedEye implementation. Observers recorded what they observed being administered (e.g., drug name, form, strength and quantity) and compared this to what was prescribed. Errors were classified as either a ‘timing’ error, ‘omission’ error or ‘other’ error. We calculated the rate and type of medication administration errors (MAEs) before and after the MedEye implementation. A sample size calculation suggested that approximately 10,000 medication administrations were needed. Data collection was reduced due to the COVID 19 pandemic and implementation delays.

Results

Trained pharmacists or nurses observed a total of 1,069 administrations of solid oral dosage forms before and 432 after the MedEye intervention was implemented. The percentage of MAEs pre-MedEye (69.1%) and post-MedEye (69.9%) remained almost the same. Non-timing errors (combination of ‘omission’ + ‘other’ errors) reduced from 51 (4.77%) to 11 (2.55%), which had borderline significance (p=0.05) however after adjusting for confounders, significance was lost. We also saw a non-significant reduction in ‘other’ error types (e.g., dose and documentation errors) following the implementation of MedEye from 34 (3.2%) to 7 (1.62%). An observer witnessed a nurse dispense the wrong medication (prednisolone) instead of the intended medication (furosemide) in the post-MedEye period. After receiving a notification from MedEye that an unexpected medication had been dispensed, the nurse corrected the dose thus preventing an error. We also identified one instance where the nurse correctly dispensed a prescribed medication (amlodipine) but this was mistakenly identified by the MedEye scanner as another prescribed medication (metoclopramide).

Conclusions

This is the first evaluation of a novel optical medication scanning device, MedEye on the rate of MAEs in one of the largest NHS trusts in England. We found a non-statistically significant reduction in non-timing error rates. This was notable because incidents within this category e.g., dose errors, are more likely to be associated with harm compared to timing errors.2 However, further research is needed to investigate the impact of MedEye on a larger sample size and range of medications.

References

1. Elliott, R., et al., Prevalence and economic burden of medication errors in the NHS in England. Rapid evidence synthesis and economic analysis of the prevalence and burden of medication error in the UK, 2018.

2. Poon, E.G., et al., Effect of bar-code technology on the safety of medication administration. New England Journal of Medicine, 2010. 362(18): p. 1698–1707.

A Qualitative Study Exploring the Barriers and Facilitators Associated with the Implementation of a Closed Loop Medication System in a UK Hospital Trust

Introduction

Implementation of novel digital technologies into complex hospital systems, particularly within the United Kingdom’s (UKs) National Health Service, is challenging and can result in delays to the use and evaluation of innovative systems. MedEye is a bedside tool for preventing medication administration errors used as part of a closed-loop medication administration system.

Aim

The aim of this study was to understand the barriers and facilitators associated with implementing MedEye for the first time in a large UK Teaching Hospital Trust.

Methods

We used a case study approach and conducted semi-structured interviews (n=9) with key members of the project implementation team from Pharmacy (n=3), Nursing (n=2), commercial system provider(n=2), IT (n=1) and academia (n=1) and 20 hours of field observations. We explored stakeholder’s experiences about the implementation process, barriers and facilitators and any key lessons learnt according to constructs from Sittig and Singh’s Eight Dimension Sociotechnical Model.1 We analysed the data from interviews and observations using the framework approach.2 We firstly familiarised ourselves with the data, coded interviews, guided by our analytical framework, charted and then interpreted the data. All necessary ethical and organisational approvals were obtained.

Results

We identified themes relating to eight sociotechnical domains. Clinical Content: the format of the medication library and process for ordering medications were different to other European sites that had implemented MedEye, posing challenges for developers. Hardware and Software Computing Infrastructure: the integration of MedEye with the electronic prescribing system was one of the “biggest challenges”(P2) and contributed to delays. Human Computer Interface: the MedEye system’s user interface was described as “clean, simple and easy to use”(P2).People: nurses and senior management “absolutely wanted this [project] to work”(P1).Communication and Workflow: it was sometimes difficult to communicate effectively because the IT team had their own “set of jargon which is very technical” and the clinical team used “lots of medical jargon”(P2), resulting in misunderstandings. Internal Organisational Policies, Procedures and Culture: the hospital recognised the potential safety benefits of MedEye. However, its implementation was different to other IT products, which would “have actually gone through the development cycle”(P7).External Rules, Regulations and Pressures: the IT and informatics team’s resources were stretched with multiple projects been implemented simultaneously. System Measurement and Monitoring: the project team conducted “a lot of testing”(P3), to refine the technology.

Conclusions

This study sought to understand the sociotechnical challenges when implementing a novel digital technology in a UK hospital and identified themes related to eight domains. We acknowledge that our study had a few limitations: we interviewed a small number of participants who were directly involved in the implementation process, and the study was conducted in one hospital Trust, limiting the generalisability of the findings. However, use of the eight-domain sociotechnical framework strengthened our study, allowing us to derive the specific facilitators and barriers to the implementation and deployment process. This study also emphasises the importance of working closely with IT managers who can coordinate work within an organisation to anticipate delays and mitigate against project risks.

References

1. Sittig, D.F. and H. Singh, A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality & safety in health care, 2010. 19 Suppl 3(Suppl 3): p. i68-i74.

2. Pope, C., S. Ziebland, and N. Mays, Qualitative research in health care. Analysing qualitative data. BMJ (Clinical research ed.), 2000. 320(7227): p. 114–116.

A survey of the knowledge and attitudes of Egyptian healthcare professionals towards the application of Health Information Technology to optimize antibiotic therapy

Introduction

Antimicrobial resistance is a global health problem, especially in developing countries. Antimicrobial Stewardship Programmes (AMS) have been shown to be effective at reducing antimicrobial resistance and hospital patient stays. Health information technology (HIT) can support Outpatient Parenteral Antimicrobial Therapy (OPAT) through more accurate diagnosis and management of infectious diseases.

Aim

To evaluate the knowledge and attitude of Egyptian healthcare professionals towards the application of HIT to optimize OPAT.

Methods

Healthcare professionals who worked in either private or public sectors of Egyptian healthcare system were emailed and asked if they would be willing to complete an electronic questionnaire (using google forms). One reminder was sent by email each week for two weeks (two in total) from the first invitation. The survey was laid out in four sections. The first section included specific details about the healthcare professional’s current employment and role, the second related to HIT services available in their organisations, the third covered their training in HIT and antimicrobial stewardship programmes, and the fourth included their use of HIT to optimize OPAT. Ethical approval was obtained from National Heart Institute, Egypt. Descriptive analysis was carried out for all the variables. One-way ANOVA testing at level of significance P-value <0.05, was used to compare numerical variables. SPSS version 26 was used for statistical analysis.

Results

Three hundred and eighty-five healthcare professionals were invited to respond to the questionnaire. (The response rate was 75.34% (290)). Of these, 152 (52.6%) were pharmacists, 134 (46.4%) physicians, and 3 (1%) nurses, and many participants (30.8%) had between 6 to 10 years of experience working in either outpatient or in-patient units. Only 15.2% of respondents mentioned that they have access to OPAT in their organizations, 51.2% did not have the service, while 33.6% responded that they did not know if the service was available. However, 27.3% had a training on ASP and 18.3% had a training on HIT. Mean scores for both knowledge (14.31±5.49) and attitude (14.67±2.53) were significantly higher in professionals who had received training in HIT (p = 0.003 & 0.006 respectively). However, scores for attitude were better than scores for knowledge.

Conclusions

In developing countries, HIT applications in OPAT are still in their infancy with only a few organisations adopting them. Egyptian healthcare professionals showed positive attitudes towards HIT use, especially when their knowledge was improved through training. Two strengths of this study include our high response rate and the wide breadth of different healthcare professionals who responded from both private and public healthcare settings. However, being a questionnaire, some questions were left unanswered and some respondents may not have been aware of their reasons for a particular answer. That said, this study has important implications for practice, with more awareness amongst healthcare professionals required on the availability of HIT services in their hospitals and how further training may be needed on the applications of HIT in optimizing OPAT.

Digital technology to support lifestyle and health behaviour changes in surgical patients: systematic review

BACKGROUND

Digital technologies (such as smartphone applications, activity trackers, and e-learning platforms) have supported patients with long-term conditions to change their lifestyle health behaviours. The aim of this study was to examine the effectiveness of digital technologies in supporting patients undergoing elective surgery to change their health behaviours.

METHODS

A systematic review was conducted of articles reporting a digital intervention supporting behaviour change in adult patients who underwent elective bariatric, oncological or orthopaedic surgery. MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, and Scopus were searched from inception to March 2019 for quantitative intervention studies with a specific focus on physical activity, dietary intake, and weight loss in patients before and after surgery (PROSPERO: CRD42019127972). The Joanna Briggs Institute critical appraisal checklist was used to assess study quality.

RESULTS

Of 3021 citations screened, 17 studies were included comprising 4923 surgical patients; these included experimental (pre-post design, feasibility studies, and RCTs) and observational studies. Three factors were identified as effective for supporting health behaviour change in elective surgical populations: digital technology delivery, implementation, and theoretical underpinning. Six of eight studies that referred to behaviour change theories observed significant improvements in health behaviour relating to reduced weight regain, and improved lifestyle choices for physical activity and diet. Meta-analysis was not possible because of heterogeneous outcome measures.

CONCLUSION

Digital technologies may effectively support behavioural change in patients undergoing elective surgery.

Surgical incidents and their impact on operating theatre staff: qualitative study

Background

Surgical incidents can have significant effects on both patients and health professionals, including emotional distress and depression. The aim of this study was to explore the personal and professional impacts of surgical incidents on operating theatre staff.

Methods

Face-to-face semistructured interviews were conducted with a range of different healthcare professionals working in operating theatres, including surgeons and anaesthetists, operating department practitioners, and theatre nurses, and across different surgical specialties at five different hospitals. All interviews were audio recorded, transcribed verbatim, and analysed using an inductive thematic approach, which involved reading and re-reading the transcripts, assigning preliminary codes, and searching for patterns and themes within the codes, with the aid of NVivo 12 software. These emerging themes were discussed with the wider research team to gain their input.

Results

Some 45 interviews were conducted, generally lasting between 30 and 75 min. Three overarching themes emerged: personal and professional impact; impact of the investigation process; and positive consequences or impact. Participants recalled experiencing negative emotions following surgical incidents that depended on the severity of the incident, patient outcomes, and the support that staff received. A culture of blame, inadequate support, and lack of a clear and transparent investigative process appeared to worsen impact.

Conclusion

The study indicated that more support is needed for operating theatre staff involved in surgical incidents. Greater transparency and better information during the investigation of such incidents for staff are still needed.

Discussion of “Attitude of Physi -cians Towards Automatic Alerting in Computerized Physician Order Entry Systems”

With these comments on the paper “Attitude of Physicians Towards Automatic Alerting in Computerized Physician Order Entry Systems”, written by Martin Jung and co authors, with Dr. Elske Ammenwerth as senior author [1], the journal wants to stimulate a broad discussion on computerized physi cian order entry systems. An international group of experts have been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.

Systematic review of psychological, emotional and behavioural impacts of surgical incidents on operating theatre staff

BACKGROUND

Adverse surgical incidents affect both patients and health professionals. This study sought to explore the effect of surgical incidents on operating theatre staff and their subsequent behaviours.

METHODS

Eligible studies were primary research or reviews that focused on the effect of incidents on operating theatre staff in primary, secondary or tertiary care settings. MEDLINE, Embase, CINALH and PsycINFO were searched. A data extraction form was used to capture pertinent information from included studies and the Critical Appraisal Skills Programme (CASP) tool to appraise their quality. PRISMA-P reporting guidelines were followed and the review is registered with PROSPERO.

RESULTS

A total of 3918 articles were identified, with 667 duplicates removed and 3230 excluded at the title, abstract and full-text stages. Of 21 included articles, eight focused on the impact of surgical incidents on surgeons and anaesthetists. Only two involved theatre nurses and theatre technicians. Five key themes emerged: the emotional impact on health professionals, organization culture and support, individual coping strategies, learning from surgical complications and recommended changes to practice.

CONCLUSION

Health professionals suffered emotional distress and often changed their behaviour following a surgical incident. Both organizations and individual clinicians can do a great deal to support staff in the aftermath of serious incidents.

Drug allergies documented in electronic health records of a large healthcare system

BACKGROUND

The prevalence of drug allergies documented in electronic health records (EHRs) of large patient populations is understudied.

OBJECTIVE

We aimed to describe the prevalence of common drug allergies and patient characteristics documented in EHRs of a large healthcare network over the last two decades.

METHODS

Drug allergy data were obtained from EHRs of patients who visited two large tertiary care hospitals in Boston from 1990 to 2013. The prevalence of each drug and drug class was calculated and compared by sex and race/ethnicity. The number of allergies per patient was calculated and the frequency of patients having 1, 2, 3…, or 10+ drug allergies was reported. We also conducted a trend analysis by comparing the proportion of each allergy to the total number of drug allergies over time.

RESULTS

Among 1 766 328 patients, 35.5% of patients had at least one reported drug allergy with an average of 1.95 drug allergies per patient. The most commonly reported drug allergies in this population were to penicillins (12.8%), sulfonamide antibiotics (7.4%), opiates (6.8%), and nonsteroidal anti-inflammatory drugs (NSAIDs) (3.5%). The relative proportion of allergies to angiotensin-converting enzyme (ACE) inhibitors and HMG CoA reductase inhibitors (statins) have more than doubled since early 2000s. Drug allergies were most prevalent among females and white patients except for NSAIDs, ACE inhibitors, and thiazide diuretics, which were more prevalent in black patients.

CONCLUSION

Females and white patients may be more likely to experience a reaction from common medications. An increase in reported allergies to ACE inhibitors and statins is noteworthy.

Standard Information Models for Representing Adverse Sensitivity Information in Clinical Documents

BACKGROUND

Adverse sensitivity (e.g., allergy and intolerance) information is a critical component of any electronic health record system. While several standards exist for structured entry of adverse sensitivity information, many clinicians record this data as free text.

OBJECTIVES

This study aimed to 1) identify and compare the existing common adverse sensitivity information models, and 2) to evaluate the coverage of the adverse sensitivity information models for representing allergy information on a subset of inpatient and outpatient adverse sensitivity clinical notes.

METHODS

We compared four common adverse sensitivity information models: Health Level 7 Allergy and Intolerance Domain Analysis Model, HL7-DAM; the Fast Healthcare Interoperability Resources, FHIR; the Consolidated Continuity of Care Document, C-CDA; and OpenEHR, and evaluated their coverage on a corpus of inpatient and outpatient notes (n = 120).

RESULTS

We found that allergy specialists' notes had the highest frequency of adverse sensitivity attributes per note, whereas emergency department notes had the fewest attributes. Overall, the models had many similarities in the central attributes which covered between 75% and 95% of adverse sensitivity information contained within the notes. However, representations of some attributes (especially the value-sets) were not well aligned between the models, which is likely to present an obstacle for achieving data interoperability. Also, adverse sensitivity exceptions were not well represented among the information models.

CONCLUSIONS

Although we found that common adverse sensitivity models cover a significant portion of relevant information in the clinical notes, our results highlight areas needed to be reconciled between the standards for data interoperability.

Discussion of "Attitude of physicians towards automatic alerting in computerized physician order entry systems"

With these comments on the paper "Attitude of Physicians Towards Automatic Alerting in Computerized Physician Order Entry Systems", written by Martin Jung and co-authors, with Dr. Elske Ammenwerth as senior author [1], the journal wants to stimulate a broad discussion on computerized physician order entry systems. An international group of experts have been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.