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Keywords:

  • blood transfusion;
  • safety;
  • errors;
  • barcode;
  • radiofrequency identification

Summary

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

Hemovigilance programs from around the world document that the greatest risk to recipients of blood transfusion is human error, resulting in transfusion of the incorrect blood component. Errors in transfusion care have strong parallels with errors in medication administration. Errors often result from ‘lapse’ or ‘slip’ mistakes in which details of patient identification are overlooked. Three areas of transfusion are focal points for improved care: the labelling of the patient's pre-transfusion sample, the decision to transfuse and the final bedside check designed to prevent mis-transfusion. Both barcodes and radio-frequency identification technology, each ideally suited to matching alpha-numeric identifiers, are being implemented in order to improve performance sample labelling and the bedside check. The decision to transfuse should ultimately be enhanced through the use of nanotechnology sensors, computerised order entry and decision support systems. Obstacles to the deployment of new technology include resistance to change, confusion regarding the best technology, and uncertainty regarding the return-on-investment. By focusing on overall transfusion safety, deploying validated systems appropriate for both medication and blood administration, thoughtful integration of technology into bedside practice and demonstration of improved performance, the application of new technologies will improve care for patients in need of transfusion therapy.


New technology for transfusion safety

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

Transfusion therapy depends upon a series of processes linking the blood donor and recipient (Dzik et al, 2003a, 2005) (Fig 1). Risks from blood transfusion have traditionally been dominated by concerns related to transfusion-transmitted infections. Fueled by media reports and governmental policies, some patients have perceived infectious risks with a sense of dread. While blood safety– safety of the fluid itself – has been the focus of attention in past decades, there is growing data-driven recognition that the transfusion processes that occur in hospitals contribute substantially to transfusion risk. The term transfusion safety, which includes blood safety, refers to the overall perspective of delivering proper transfusion care.

image

Figure 1.  Transfusion Safety: process and product. Safe transfusion depends upon a series of linked processes and includes more than just blood safety. Numbers 1–3 refer to the three zones of error in the process of transfusion. See text for details.

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Currently, the greatest transfusion risks to patients derive from unsafe practices in hospitals. There are three major ‘zones of error’ jeopardising safe transfusion: (i) accurate patient identification and proper labelling of the pre-transfusion specimen; (ii) appropriate decision-making regarding the clinical use of blood components; and (iii) accurate bedside verification that the correct blood is to be given to the intended recipient. New technologies designed to improve performance in each of these areas are discussed below. However, it should be emphasised that enhanced transfusion safety vitally depends upon how people use new technology. Indeed, the skillful integration of technology systems into human behaviour during the care of sick patients is one of the important healthcare challenges for the next decade.

We all make mistakes

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

Errors in medicine can be broadly categorised as either knowledge errors or slip errors. Knowledge errors occur when humans think as ‘problem solvers’. These errors result from lack of knowledge or incomplete information or faulty reasoning. Knowledge errors in transfusion result in inappropriate decisions to administer blood. In contrast, slip errors occur when humans think ‘in automatic mode’. Slip errors result from distraction, fatigue, or inattention. For example, when driving an automobile in an unfamiliar region, one can take the wrong road and get lost by making knowledge-based errors. In contrast, a slip error might be the failure to stop at a posted ‘stop sign’ or failure to signal when changing lanes. Slip errors tend to be ‘rule breakers’. Automobile injury and death are much more likely to be due to slip errors than cognitive errors. Humans ‘know better’ when they make slip errors. However, slip errors – as the automobile example demonstrates – can be quite deadly and sometimes only good luck prevents a bad outcome. Slip errors in transfusion practice result in mis-labelling of blood samples and in the administration of the wrong blood to an unintended recipient. Slip errors caught in time or without harm are called ‘near-miss’ events. The Medical Event Reporting System for transfusion has documented that near-miss events are 300 times more common than observed adverse events (Kaplan, 2005). Thus, near-miss events are a rich area for information on process failure. Not all medical errors will result in an adverse event for the patient. Thus, hemovigilance systems that tabulate reported adverse events consistently under-estimate the actual frequency of medical errors.

Although transfusion recipients are harmed by both kinds of error, Dzik (2003, 2005 has noted that the technology approaches to reducing such errors are quite different. Reduction in transfusion-related knowledge-based errors depends on increased knowledge, more informed decision-making, and enhanced feedback on decisions. Increased safety is expected to result from the use of enhanced information transfer, new technology to monitor patient physiology pertinent to transfusion, and computerised blood usage review and education. Reduction in slip errors is ideally suited to machine-readable identification systems. Machines cannot be distracted, do not make assumptions that lead to error, and are much better suited than humans to repetitive data matching. Thus, while patients are the victims of human error, healthcare workers can be both the targets and the casualties of systems which are highly error prone and in need of re-engineering. An important goal of patient safety initiatives is to understand how systems fail people and to redesign the delivery of healthcare in ways that restore the alliance between the healthcare worker and the patient.

Demographics of error

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

In a landmark publication (Kohn et al, 2000), the Institute of Medicine provided a snapshot of errors in healthcare at the close of the 20th century. The report cited that as many as 98 000 patient deaths occurred annually in the United States as a result of errors by healthcare workers. A subsequent report by Health Grades, Inc. (2006) put the estimate even higher and found it to be rising annually.

The cost of treating healthcare errors is estimated to be in the range of $3 billion per year in the United States – of which over $1 million is estimated to be avoidable cost. Health Grades, Inc., 2006. Despite the sheer magnitude of the problem and despite the evidence from other sectors of society – such as the airline industry, the energy industry and retail industry – that technology can substantially reduce the rate of human error, hospitals have been slow to adopt patient safety technology. Nevertheless, leadership in healthcare has begun to awaken to the task. In the United States, for example, the Joint Commission on Accreditation of Hospitals has, for the last four consecutive years, placed ‘improved accuracy of patient identification’ as its number one priority for patient safety. This goal remains the Joint's Commission top hospital patient safety priority for 2007 (http://www.jointcommission.org/PatientSafety/NationalPatientSafetyGoals/07_hap_cah_npsgs.htm). In a similar spirit, the annual leadership survey of the Health Information Management Systems Society – a professional society devoted to hospital information technology – has, for the last several years, identified improved patient safety and reduction of medical errors as its highest short-term priority (http://www.himss.org/2006survey/).

Medical errors do not affect all patients with equal frequency. Critically ill patients, who are physiologically the most vulnerable to healthcare error, also receive the most intense and complex treatments and Cullen et al (1997) has noted that errors are more common among intensive care patients. Among critically ill patients, the rates of error – including those resulting in fatal outcomes – are unacceptably high. For example, Rothschild et al (2005a)) reported on treatment practices in the intensive care unit of an advanced, academic hospital. Care of 391 patients (1500 patient-days) was observed. One in five patients suffered at least one adverse event, although many patients experienced multiple events. Serious errors (n = 223) were found to occur at a rate of 150 per 1000 patient-days, representing an event every 8 h for a 30-bed treatment unit. 11% of serious errors were life-threatening or fatal. The most common life-threatening errors were related to medication delivery and resulted from ‘slip errors’ rather than cognitive failures. The data on transfusion errors also cluster around urgent care as a higher proportion of such patients receive transfusion. For example, reports of fatal transfusion mishaps submitted to the US FDA repeatedly demonstrated that the operating room is a common location for fatal transfusion mistakes (Sazama, 1990, 2003).

The concentration of medical errors in urgent care settings – emergency rooms, operating rooms and intensive care units – presents an upside to the deployment of new technology for patient safety. Because the majority of errors are concentrated on a limited number of patient beds, these areas represent ideal locations for the validation and application of new technology. Moreover, hospitals should recognise that they need only initially invest in a limited number of bed areas in order to achieve substantial return on their investment.

The transfusion service and the pharmacy share common ground.

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

Very strong parallels exist between safe transfusion and safe medication therapy. The hospital transfusion service and the pharmacy are likely to be more effective in promoting new approaches to patient care by joining forces and adopting new technologies that serve the needs of both transfusion and medication administration.

Medication errors are extremely common. Medication errors, like transfusion errors, occur at all steps of the process. For example, Cina et al (2006) reported error rates determined from direct observational audits in a large hospital pharmacy. Among 140 755 medication orders filled, over 5000 (3·6%) contained errors. The hospital pharmacist detected only 79% of these errors during verification and checked prior to release from the pharmacy. Review of the errors demonstrated that 24% were capable of potential adverse drug events and 1% were life-threatening. The most common error was incorrect medication.

Recently, hospital pharmacies have begun to apply bar code technology to individual dose medication packaging. To the transfusion medicine professional, accustomed for decades to bar codes on blood bags, this change may seem to have been overdue. Based on experience with bar coding on blood bags, however, one might anticipate that bar codes on medications will not fulfil their full potential until machine-readable technology is deployed at the bedside. Nevertheless, there is evidence of improvement in dispensing errors within the pharmacy. For example, at the Brigham and Women's hospital in Boston, Poon (2006) compared dispensing errors before and after the implementation of bar coding on medication containers. Prior to bar coding, 0·37% of 115 164 doses had target dispensing errors. After bar coding, errors dropped to 0·06% of 253 984 doses. Because the hospital dispenses 6 million medication doses per year, the technology is expected to prevent 13 000 dispensing errors and 6000 potential adverse drug events annually.

Error zone # 1: blood samples collected for pre-transfusion testing

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

Case summary

A woman in a two-bed hospital room in Virginia changed her bed in order to be closer to the window. This innocent act, coupled with a slip error by the phlebotomist, resulted in a mis-labelled pre-transfusion specimen and cross-matching of blood of the incorrect ABO group. When she was transfused, she suffered a major reaction and died.

Errors in blood sample collection are especially dangerous as they can initiate a process which is wrong from the very first step. Linden et al (2000) documented that 14% of ABO incompatible transfusions reported to New York State were due to sample collection errors.

Two broad categories of errors are recognised: mis-labelled samples (in which the label on the tube does not meet local criteria for acceptance) and mis-collected samples (in which the sample is correctly labelled, but contains the blood of an individual different from the person named on the tube). The latter error, also termed WBIT (Wrong Blood in Tube), is particularly dangerous because the properly labelled WBIT sample will be accessioned into the laboratory. WBIT samples are identified by comparing the ABO/Rh results of the sample with historical results on record for that patient. The observation of a WBIT represents a very serious near-miss event. WBIT samples on patients with no prior results on record are the most dangerous samples but cannot even be detected under routine circumstances.

A multicenter international study of 650 000 samples submitted to transfusion facilities reported that the median frequency of mis-labelled samples among participating hospitals was 1 in 165 (6·1 per 1000; interquartile range 1·2–17 per 1000); and that the median frequency of mis-collected (WBIT) samples was 1 in 1986 (0·5 per 1000; interquartile range < 0·3–0·9 per 1000) (Dzik et al, 2003b). Similar data was reported for the UK (Murphy et al, 2004) where the frequency of WBIT samples was estimated to be 1 in 2082. In a single institution study at Johns Hopkins, 1·4% of 40 770 samples (1 in 71) were mis-labelled and 0·035% (1 in 2800) were WBIT (Lumadue et al, 1997). The authors noted that samples with labelling errors were 40-fold more likely to have WBIT. This important finding underscored the rational for laboratories to systematically refuse to process mis-labelled samples. Another study from a centralised laboratory in France reported that 1 in 3400 samples had identified ABO discrepancies based on comparison with previous records (Chiaroni et al, 2004).

Technology for improved sample labelling

The use of handheld devices to capture bar-coded patient identification from the patient's wristband, coupled with the use of small printers that create specimen labels at the bedside using data taken directly from the patient's wristband, is a natural technology for improved sample labelling (Fig 2). Several commercially available products are being deployed by hospitals and others are in development. A recent survey of products available in the USA has been published (Aller, 2005).

image

Figure 2.  A bar-code based system for bedside labelling of blood samples. Photo courtesy of Lattice Corp, Wheaton, IL, USA.

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The handheld device of a typical commercially available system receives a list of patients whose specimens need to be collected and transmits collection times to the laboratory. This requires an interface between the bedside handheld device and the hospital network. The nature of this interface can be an important decision for hospitals. Wireless communication – either using an existing 802.11 network (such as is used for cellular phone communication) or using a dedicated wireless installation – is used by some systems. In other systems, the handheld device is intermittently placed in a ‘cradle’ which then transfers information via cable to the hospital network.

Statistical process control (SPC) charts and national performance standards

Statistical process control charts are an established mechanism for tracking performance and quality control. SPC technology could easily be adapted to track the performance of proper sample collection and research exploring this approach needs to be done. Laboratories can readily capture the frequency of mis-labelled or WBIT samples. Using SPC, data collected during a baseline period are used to establish an upper-control limit of performance. On-going periodic monitoring can then be used to document hospital performance, to identify periods when process falls out of control, and to document the effects of interventions designed to improve safety. Patients would benefit by the development of national professional performance standards in the area of patient identification and sample collection. Currently, no nation has provided national standards defining the minimum expected performance for proper sample labelling. A national standard could be easily incorporated into SPC monitoring, giving hospitals clear guidance on their performance relative to their peers and providing an objective basis for taking action to investigate and re-engineer faulty systems.

Error zone # 2: the decision to transfuse

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

Case summary

A 58-year-old man undergoing coronary bypass surgery received two units of Fresh Frozen Plasma at the start of surgery in response to a measured International Normalized Ratio (INR) level of 1.3. Within 30 min of the transfusion, he developed severe transfusion-associated acute lung injury and required a ‘crash’ conversion to cardiopulmonary bypass. At the end of surgery his chest could not be closed due to inadequate ventilation and he was transferred to the recovery area with a split-sternum open chest. He developed mediastinitis, had a prolonged hospital stay, but survived and was discharged.

New technologies hold great promise to improve the decision-to-transfuse. These include enhanced patient monitoring, physician order-entry and computer-based decision assessment and feedback.

Tissue oxygen probes/nanosensors

Although Red Blood Cells (RBC) are administered with the intention of improving tissue oxygenation, current transfusion decisions are driven largely by measurements of the blood haemoglobin concentration, even though this value represents only one determinant of tissue oxygenation. Clinicians can look forward to the day when nanotechnology tissue oxygen sensors will be implanted in informative critical organ beds to supply real time data on oxygenation. The notion of ‘transfusion based on a haemoglobin level’ will then appear very obsolete.

Early clinical application of oxygen sensors has begun. Using a small needle probe, Suttner et al (2004) measured the partial pressure of oxygen in the deltoid muscle of 51 patients after cardiac surgery. Patients with a haemoglobin concentration of 75–85 g/l were randomly assigned to three groups: group 1 received one unit of RBCs and 40% inspired oxygen; group 2 received two units of RBCs and 40% oxygen; and group 3 received no transfusion and 100% inspired oxygen. During 3 h of observation, the investigators found that transfusion increased the circulating oxygen delivery but had no effect on tissue oxygen. In contrast, 100% oxygen without transfusion significantly increased tissue oxygen levels from an average 24 mmHg to 34 mmHg. Smith et al (2005) reported results on 35 consecutive patients who received packed RBC transfusions during neurosurgery while undergoing continuous monitoring of the partial pressure of oxygen in brain tissue. A small probe placed in cerebral white matter provided the information on tissue oxygen levels. Following red cell transfusion, brain tissue oxygen increased in 26 patients (75%). The mean increase was 3·1 mmHg from a baseline pO2 of 29·1 mmHg.

Computerised physician order entry (POE)

Computerised POE has been shown to reduce serious errors by 50% and all errors by 80% (Bates et al, 1998, 1999). Computerised POE offers a more structured, legible, and traceable communication between physicians and the blood transfusion service. Computerised POE provides additional advantages when combined with computer-assisted decision support. Decision support provides passive but readily available information intended to assist the clinician in making a proper decision. Examples include drug-allergy alerts, dose-range recommendations, antibiotic selection options and user-adjustable order templates. In transfusion medicine, such alerts can provide valuable guideline information at the time of blood requests and provide feedback to clinicians on indications for transfusion.

A recent randomised controlled trial tested the value of electronic decision alerts sent to physicians caring for patients at risk for venous thromboembolism (Kucher et al, 2005). In the test group, the computer alerted physicians to the patient's risk and advised physicians to consider prophylaxis treatment. No alert or advice was provided to the control group. The study found that more patients in the test group were treated with pneumatic compression boots and/or heparin. Furthermore, the study found that patients treated by physicians in the intervention group had a lower frequency of clinical venous thromboembolic events.

Despite the apparent advantages of computerised entry of physician orders, a 2003 survey by the Leapfrog Group found that only 3·7% of 842 member hospitals in the USA had fully implemented a computerised order entry (Hillmann & Given, 2005). Obstacles to implementation include the high cost and complexity of ‘intelligent’ computerised POE.

Computerised review of blood utilisation with clinician feedback

Current methods used to assess the decision-to-transfuse are quite rudimentary. Nevertheless, even simple measures affect physician ordering behaviour (Tinmouth et al, 2005). Thus, more sophisticated approaches hold great promise to improve care. Information technology is poised to improve the utilisation of blood components through a process of computerised blood usage review and clinician feedback. Teaching proper transfusion therapy in the context of actual decisions made by the clinician may prove far more effective than classroom style education. While there will be many variations on the theme, the approach will be to use information systems to gather relevant clinical data on patients who are given transfusions (as well as those not given transfusions) and then to ‘gate’ this data through an algorithm designed to identify transfusion decisions that were likely to be appropriate and those that were not. The ordering physician can then receive feedback that includes educational information targeted to the decision. In the presence of a robust information system environment, the algorithms to be used for assessing physician decisions can be flexible, sophisticated and pertinent to each individual recipient. For example, the decision to administer red cells to a non-bleeding intensive care patient may include the patient's age, gender, diagnosis, haemoglobin concentration, pulse, blood pressure, cardiac output (if known), troponin levels, oxygen saturation, ventilator settings, lactate levels, arterial pH and other clinical and physiological variables. Physician feedback may include data summaries on similar patients, anonymized peer comparison data, and links to relevant publications.

A daily, computerised assessment of all transfusion decisions was implemented at the Massachusetts General Hospital in Boston in 1999. The algorithm used to filter transfusions for review is not as sophisticated as it could be, but does include age-adjusted pre-transfusion haemoglobin cut-off points for RBC transfusions. Cases not meeting the algorithm (approximately 5% of transfusions) are reviewed daily by a physician who considers the post-transfusion data. The cases that remain after physician review are then used for direct email feedback to the physician who ordered the transfusion. The feedback provides a link to an intranet site which contains selected clinical research papers on best transfusion practice. Coincidental with the use of this approach, the number of transfusion decisions not meeting the algorithm has steadily declined. A somewhat similar system designed to address decisions in a neonatal intensive care unit was reported (Peteja et al, 2004). Another approach has used basic models of neural networks to extract the important elements of the decision to transfuse (Etchells & Harrison, 2006).

Error Zone #3: giving the right blood to the right patient

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

Case summary

Four units of group A red blood cells intended for Operating Room # 42 were delivered by mistake to Operating Room # 44, located directly across the hall from room # 42. The anaesthesiologist in room # 44 had also urgently requested blood for his patient and assumed that the delivered units were the correct ones. The recipient, who was group O, suffered a major haemolytic reaction and did not survive.

Mis-transfusion, in which the wrong blood is transfused to the recipient, is the single most frequent error resulting in ABO-incompatible transfusions and is one of the leading causes of morbidity and death resulting from blood transfusion. Mis-transfusion errors accounted for nearly 40% of the ABO-incompatible transfusions reported by Linden et al (2000) who estimated that 1 in 14 000 transfusions involved ABO errors. Data from hemovigilance programs in England (Serious Hazards of Transfusion (SHOT), http://www.shotuk.org), in the USA by Sazama (1990), in Canada by Robillard et al (2004), and in France by Andreu et al (2002) showed similar rates and documented that mis-transfusion errors were a common and substantial risk to patients.

In the UK, the SHOT programme has consistently demonstrated the magnitude of transfusion errors. For the 2004 survey, incorrect blood component transfused was by far the leading serious hazard, representing an astounding 81% of reported errors (http://www.shotuk.org/) (Fig 3). Although not all mis-transfusion events directly harm patients, the absence of harm is no excuse to tolerate an error-prone process capable of harm. When only those events that result in major organ failure or death are considered, mis-transfusion remains the largest cause of serious adverse outcomes for transfused patients. The tragedy that accompanies these events is compounded by the fact that they are preventable.

image

Figure 3.  Serious hazards of transfusion (1996–2001). The relative frequency of different transfusion risk is shown. Data available fromhttp://www.shotuk.org/index.htm TRALI, transfusion-related lung Injury; GVHD, graft-versus-host disease.

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The bedside check, which verifies that the identity of the intended recipient matches the identity of the person about to receive the transfusion, is the most critical step to prevent mis-transfusion events. It represents the final opportunity to interrupt a mis-guided blood component. Failure to perform the final bedside check risks transfusion of blood products without ABO safeguards – akin to the state of transfusion care in the 19th century. Nevertheless, failure to perform the bedside check correctly is surprisingly common. Miller (2000) and Novis et al (2003), on behalf of the College of American Pathologists, published results of two large observational audits. These audits assessed the frequency with which basic elements of the bedside check were performed, including positive patient identification, matching wristband identification to the blood compatibility label, matching patient identifiers with the blood request, and review of compatibility and expiration date information. In the year 2000, the audit of over 4000 transfusions revealed a failure to match wristband identification with the compatibility label in 25% of transfusions. Among advanced healthcare systems worldwide, this translates into literally millions of episodes each year in which patients are not provided basic safeguards against the most common serious hazard of transfusion. Machine-readable identification technology is ideally suited to the needs of the bedside check. Two identification technologies – bar coding and radiofrequency identification (RFID) – hold great promise to enhance patient safety.

Bar code at the bedside

Bar code technology is a widely used, stable, inexpensive means of machine-readable identification. Bar coding is already ubiquitous in the retail sector. A supermarket checkout slip is undoubtedly more accurate than a patient's medical record. Obstacles to implementation of bar coding are described below. Nevertheless, gradual progress is being made to apply bar code technology to patient identification and transfusion/medication safety. Progress by technology-forward hospitals is being stimulated by the availability of a number of commercial products developed specifically for healthcare (Table I).

Table I.   Commercially available products using bar-code technology to improve transfusion safety. The listing is representative and does not include all companies.
CompanyWebsite
AMT Systems: PatientSafe TransfuseIDhttp://www.amtsystems.com/Health/TransfuseID.htm
Becton Dickinson BdiDhttp://www.bd.com/bdid/
Bio-Logics Identi-matchhttp://www.biologicsinc.com/
Bridge Medical MedPointhttp://www.bridgemedical.com/
Care Fusion BloodCarehttp://www.carefusion.com/home.asp
Korchek Technologies CareChekhttp://www.korchek.com/
Lattice Corp MediCopiahttp://www.lattice.com/index.html
Precision Dynamics Scanbandhttp://www.pdcorp.com/index.html

The John Radcliffe Hospital, Oxford (UK) has been a leader in the application of bedside barcode technology to improve transfusion safety. As described by Murphy and Kay (2004), patients admitted to an outpatient transfusion unit are given wristbands with barcode and eye-readable identification. The transfusion slip attached to the unit by the laboratory also contains bar-coded information of the identity of the intended recipient. At the bedside, a single nurse scans the patient and the unit, using a hand-held bar code reader, to verify the accuracy of the bedside check (Fig 4). Observational audits of the completeness of the bedside check were done before and after implementation of the technology. The audits documented both acceptance of the new technology and improved performance of the bedside safety check. A similar system has been installed at Georgetown University hospital in Washington. (Sandler et al, 2005).

image

Figure 4.  A bar-code based system for the bedside transfusion check in use at the John Radcliffe Hospital, Oxford, UK. Photo courtesy of Michael F Murphy, MD, USA.

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Chan et al (2004) reported a simplified barcode-based system that reduced error rates in a hospital in Hong Kong. They found that compliance with the system averaged 90%. Problems were encountered with battery failures of handheld devices. The University of Iowa recently introduced a similar barcode-based system using hand-held devices that connect to the hospital information system using wireless technology. Following an 8-month pilot evaluation, users estimated that the system captured errors up to 10 times better than the previously used manual process. Hospital-wide deployment began in 2005 (Porcella & Walker, 2005).

Despite these successes, barcode systems have their disadvantages and in some studies were found to have not improved performance (Nichols et al, 2004). Barcodes on wristbands can become blurred and can be difficult to position properly for reading. Barcode interrogation of a wristband requires ‘two hands’– one to hold the band and one to hold the reader – requiring that the blood unit be put aside. Barcodes on blood units and medications are confusing because each item delivered to the floor usually contains multiple different barcodes, including those that were relevant for earlier steps in the production process, but are a distraction for the bedside check. Barcode-based systems require that the wristband and the unit (or medication) be scanned each an every time. In urgent care areas, the process can feel clumsy.

Radiofrequency identification comes to healthcare

Radiofrequency identification technology has several theoretical advantages compared with bar coding for patient safety applications. First, an RFID chip can hold substantially more data than a bar code and could include not only patient identification data, but also information such as blood group, allo-antibodies, special blood needs and drug allergies. Second, RFID chips do not require that the user align a light beam with a bar code (line of sight) and are therefore much more user-friendly than bar codes. For this reason, RFID is already favoured over bar coding in other situations in which people move – such as automobile entry into restricted parking areas and keyless door access. Third, because RFID chips are interrogated simply by proximity to an electronic reader, they are better suited than bar code technology to a passive-system approach in which the user is not required to ‘do something.’ As one example, RFID ‘portal technology’ places the chip reader in the doorway to a room. Anyone entering the room carrying a chip-encoded object could have the chip read simply by walking through the doorway. Thus, carrying the wrong blood unit into a patient's room could trigger an alarm.

The use of two kinds of RFID technology – with two different goals – are expected in healthcare (Anonymous, 2005). Active RFID refers to technology in which the chip is battery powered and emits energy that can be read over a distance. (A cellular phone is an example of an active RFID device.) Active RFID ‘tags’ can broadcast their location and their identity. In the hospital, active tags are being used for ‘asset tracking’. Tags are placed on valuable equipment. Detection of the tag's location requires that the hospital be outfitted with chip readers located throughout the hospital. Despite the considerable infrastructure that must be deployed in order to position tag readers throughout a large hospital building, the ability to track and identify valuable equipment has convinced some hospital managers that costs are rapidly recovered. Active tags can also be placed on hospital staff and on patients. However, this introduces technological surveillance with its associated disadvantages.

Passive RFID technology is more suited to blood bags, medications and patient wrist-bands. Passive tags emit no energy. They are read only when brought to close proximity of an electronic RFID reader. Security access cards and anti-theft clips on retail clothing are examples of passive tag technology. They are disposable and cost a fraction of the price of active tags. Passive tags, also called ‘smart labels’, are ideally suited to replace bar code technology. At Massachusetts General Hospital (Boston), we have performed a pilot study in the Operating Theatre in which passive tags have been applied to patient wristbands and to blood bags intended for transfusion. A bedside chip reader was used to compare the identity data on the wristband with the identity data on the bag. The user has only to wave the blood bag in front of a computer to receive feedback on the identity match between patient and bag Dzik et al, 2005.

A passive RFID tag system, designed exclusively for autologous blood, was piloted at San Raffaele hospital in Milan, Italy (Dalton et al, 2005). Autologous blood donors were given a RFID wristband and a tag was placed on their donor unit at the time of donation. Later, at the time of transfusion, the data on the wristband and the blood unit were compared with the bedside using a hand-held RFID reader. The reader communicates with hospital computers via a hospital-wide wireless network already in place. A pilot program designed for allogeneic transfusions is also underway at Saarbrucken, Germany (http://www.rfidjournal.com/article/articleview/2169/1/1). Passive RFID tags are applied to the patient's wristband and to blood bags. At the time of transfusion, a hand held RFID reader is used to interrogate both the wristband and the bag. The reader communicates with the hospital computers via a wireless LAN already in place at the hospital. Another RFID-based program, designed for outpatient allogeneic transfusions, is under investigation at Georgetown Medical Center in Washington (http://www.cap.org/apps/docs/cap_today/feature_stories/0705RFID.html).

Other approaches: smart pumps

While most programs under development employ a bedside computer to compare data and display results, another approach is to embed safety technology in mechanical ‘pumps’ used to deliver medications and blood products. Smart pumps can read bar coded or RFID-coded data placed on medication bags, blood bags and the patient's wristband to ‘decide’ whether the infusion is appropriate for the patient. By receiving data from the patient's electronic medical record, smart pumps can verify that there is no known allergy to the medication about to be given, that the dose and rate of administration are appropriate for the age and body mass of the recipient, and that the time of administration is consistent with the medical order. Smart pumps offer the advantage that if the medication does not meet specifications, they will not function. However, smart pumps have the disadvantage that not all therapies are infused with pumps and so they do not represent a ‘universal platform’ for checking all medications and transfusions. Indeed, in a randomised controlled trial conducted in a cardiac intensive care unit, (Rothschild et al, 2005b) found that the use of smart pumps failed to decrease the rate of medication errors largely due to poor compliance with use of the technology.

Obstacles to implementation of bedside patient identification technology

Despite the apparent advantages to barcode-based or RFID-based bedside technology designed to enhance proper patient identification, acceptance and implementation of such technology has been surprising very slow (Dzik, 2005). At least three obstacles need to be overcome in order for the technology to gain more widespread acceptance.

First, healthcare workers need to embrace the culture of using technology for patient care. Nurses and physicians may feel that they ‘do not need’ the help of machines to verify medication and transfusion information. Nurses, in particular, may misunderstand the rational for using technology checks, viewing such checks as an implication that they do not ‘know’ their own patient, rather than understanding that the true purpose of the check is to examine whether or not an error was made by the laboratory (or pharmacy) that dispensed the product or by the transport system that delivered the product to the bedside. Healthcare workers may feel that things are ‘OK’, tending to underestimate the magnitude and frequency of errors perhaps because of the conviction of good intentions. Healthcare workers may see the introduction of machine-readable patient identification technology as ‘dehumanising’ the care of the patient. and may chose not to use poorly designed, clumsy, or complicated technologies that interfere with the basic care of the patient. Even good system design will fail without adequate staff education and the acceptance of technology.

Second, information system managers need clarification on what technologies should be selected. There are many different ways to implement machine readable technology systems and decisions regarding extent of deployment, range of application, networking, infrastructure redesign and system longevity are complex. Evidence for the quality of technology design and ease of integration into the hospital environment is often scant.

Third, government leaders and hospital administrators need information on the return-on-investment (ROI). Healthcare errors include many hidden costs, making true estimates of the cost of human error difficult to quantify. Nevertheless, current estimates highly favour deployment of systems – especially if targeted to those areas providing urgent and complex care. The HIMSS projected that implementation of bedside barcode systems designed for medication and transfusion care would cost approximately $2000 per bed. Thus a 200 bed facility would spend $400 000. It was further estimated that a facility that admits 20 patients a day could expect to incur $700 000 in annual expense related to adverse drug events. Thus, if bedside machine-readable technology only prevented half of such errors, the ROI would be approximately one year. If the technology focused on urgent care areas, where it will achieve the highest benefit, the ROI would be measured in months.

New technology – if poorly designed or poorly implemented – can introduce errors.

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

While there is no doubt that the ‘slip errors’, which contribute substantially to patient morbidity, mortality and excess healthcare cost, can be dramatically reduced through the application of new technology, this does not mean that all technology is good. Everyone is familiar with poorly written software that requires workarounds, that misleads, and that can increase the chance of human error. Computerised order entry systems must be designed with input from practicing physicians or errors will increase Ash et al, 2004. One study demonstrated that poorly designed computerised POE facilitated 22 types of medication errors, and that such errors were made by 75% of users (Koppel et al, 2005). In another example, we demonstrated that a software program designed to control dispensing of blood components was not keyed to the patient's medical record number and thereby facilitated the release of incorrect units (Dzik et al, 2004).

Concluding remarks: more than new technology is needed

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives

New technology alone will not improve transfusion safety. Better patient care ultimately depends on dedicated professionals using advanced technology and working within systems designed to reduce error. Hospitals will need sufficient staffing, transfusion safety officers, and enlightened administrative leadership in order for the best technologies to be selected, validated, implemented, and enhanced over time. At the national level, professional societies need to establish standards of performance for key outcomes that serve as indicators of error. No individual employed in healthcare comes to work with the intention of harming a fellow human being who is sick and vulnerable. Nevertheless, the data are conclusive that such harm occurs every day. Prevention of tragic errors is not an idealised goal to be awaited, but a deliberate path to follow. That path requires that we refocus our attention on transfusion safety, rather than just blood safety; that we confront the importance of human error in our practice; that we accelerate the implementation of technologies shown to be of value; that we establish professional standards of performance; and that we place the welfare of the patient as our highest goal.

References

  1. Top of page
  2. Summary
  3. New technology for transfusion safety
  4. We all make mistakes
  5. Demographics of error
  6. The transfusion service and the pharmacy share common ground.
  7. Error zone # 1: blood samples collected for pre-transfusion testing
  8. Error zone # 2: the decision to transfuse
  9. Error Zone #3: giving the right blood to the right patient
  10. New technology – if poorly designed or poorly implemented – can introduce errors.
  11. Concluding remarks: more than new technology is needed
  12. References
  13. Internet resources for more information on patient safety initiatives
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