Frequency of Errors in Medicine Using Information Technology
Literature strongly suggests that the frequency of error in medicine is substantial. Even more concerning is the frequency of respective harm caused by medical errors. Accordingly it is not difficult to connect frequency of medical errors to overall quality of patient care.
The following article Reducing the Frequency of Errors in Medicine Using Information Technology discusses the use of information systems to reduce the number of medical errors and their adverse implications. For this case assignment read through the article and wirte a scholarly paper that addresses the following:
1. Identify three to four common sources of medical errors and their potential implications.
2. Describe ways that information technology and systems can be used to help mitigate these errors.
3. Beyond reducing errors describe other ways the health information systems and discipline of Health Informatics as a whole can improve the quality of patient care.
Dr. William Hersh provides a good overview of Health Informatics and its contribution to improving health care in the following article. Improving Health Care 1
In this article Ronald Johnson presents a good summary of Health Informatics and clinical care innovations both past and future and relationship to areas of health care. History of Clinical Innovation 2
Three generations of concepts related to workflow are discussed in this article. The article also provides an overview of how these workflows are integrated among departments and disciplines. The significance of data quality reliability and availability is also addressed. Integration and Beyond 3
This paper discusses survey research and analysis conducted in three university surgery clinics that examined the structure roles processes and workflow. Workflow Analysis 4
The implications for informatics in the context of clinical research are discussed in this paper. In this paper current and predicted connections are made based on the increased interoperability of systems and access to high quality information. Implications for Informatics 5
Patient and healthcare information are highly sensitive and confidential. Accordingly the Health Insurance Portability and Accounting Act (HIPAA) imposes new regulatory rules upon healthcare providers and participants. The rules target at Transaction Standards Privacy Regulations and Security Requirements. Essentially HIPAA give patients the right to access their health care information while protecting the access and use of the information by others. The following links provide an overview of HIPAA and its implications.
o HIPAA1: General overview of HIPAA. 6
o HIPAA2: Department of Health and Human Resources Summary (pdf) 7
Charlene Mariette offers a good overview of HIPAA and implementation challenges faced by organizations. Blueprint for Privacy and Security 8
Sources Referenced Above
1. Hersh W. R. (2002). Improving Health Care Through Information.Journal of the American Medical Association 288 (16). Retrieved July 3 2004 the JAMA Library database.
2. Johnson Ronald. (2003). Health Care Technology: A History of Clinical Care Innovation. Health Technology: Special Technology Overview HCT Project 1. Retrieved on August 12 2009
3. Stead W. Miller R Musen M & Hersh W. (2000) Integration and Beyond: Linking Information from Disparate Sources and into Workflow. Journal of the American Medical Informatics Association 7 135-145.
4. Mueller M. L. Ganslandt T. Frankewitsch T. Krieglsein C. F. Senninger N. & Prokosch H. U. (2003). Workflow Analysis and Evidence-Base Medicine: Toward Integration of Knowledge-Based Functions in Hospital Information Systems. Department of Medical Informatics and Biomathematics University of Muenster Germany.
5. Rindfleish T. C. and Brutlag D. L. (1998). Directions for Clinical Research and Genomic Research into the Next Decade. Journal of the American Medical Informatics Association 5 404-411.
6. HIPAA Overview. (2003). U.S. Department of Health and Human Services.
7. Summary of the Privacy Rules. (2003). U.S. Department of Health and Human Services
8. Marietti C. (2002). Blueprint for Privacy and Security. Health Informatics 19 (1) 55-60. Retrieved August 5 2007 from PubMed database.
HEALTH INFORMATICS IN THE CLINICAL ADMINISTRATIVE AND RESEARCH SETTINGS
Quality of Health Care
Reducing the Requency of Errors in Medicine Using Information Technology
Bates D. W. Cohen M. Leape L. L. Overhage J. M. Shabot M. M. & Sheridan T. (2001). Reducing the requency of errors in medicine using information technology. Journal of the American Medical Informatics Association 8 299-308.
Background: Increasing data suggest that error in medicine is frequent and results in substantial harm. The recent Institute of Medicine report (LT Kohn JM Corrigan MS Donaldson eds: To Err Is Human: Building a Safer Health System. Washington DC: National Academy Press 1999) described the magnitude of the problem and the public interest in this issue which was already large has grown.
Goal: The goal of this white paper is to describe how the frequency and consequences of errors in medical care can be reduced (although in some instances they are potentiated) by the use of information technology in the provision of care and to make general and specific recommendations regarding error reduction through the use of information technology.
Results: General recommendations are to implement clinical decision support judiciously; to consider consequent actions when designing systems; to test existing systems to ensure they actually catch errors that injure patients; to promote adoption of standards for data and systems; to develop systems that communicate with each other; to use systems in new ways; to measure and prevent adverse consequences; to make existing quality structures meaningful; and to improve regulation and remove disincentives for vendors to provide clinical decision support. Specific recommendations are to implement provider order entry systems especially computerized prescribing; to implement bar-coding for medications blood devices and patients; and to utilize modern electronic systems to communicate key pieces of asynchronous data such as markedly abnormal laboratory values.
Conclusions: Appropriate increases in the use of information technology in health care?? especially the introduction of clinical decision support and better linkages in and among systems resulting in process simplification??could result in substantial improvement in patient safety
Our goal in this manuscript is to describe how information technology can be used to reduce the frequency and consequences of errors in health care. We begin by discussing the Institute of Medicine report and the evidence that errors and iatrogenic injury are a problem in medicine and also briefly mention the issue of inefficiency. We then define our scope of discussion (in particular what we are considering an error) and then discuss the theory of error as it applies to information technology and the importance of systems improvement. We then discuss the effects of clinical decision support and errors generated by information technology. That is followed by management issues the value proposition barriers and recent developments on the national front. We conclude by making a number of evidence-based general and specific recommendations regarding the use of information technology for error prevention in health care.
The Institute of Medicine Report and Iatorgenesis
Errors in medicine are frequent as they are in all domains in life. While most errors have little potential for harm some do result in injury and the cumulative consequences of error in medicine are huge.
When the Institute of Medicine (IOM) released its report To Err is Human: Building a Safer Health System in November 19991 the public response surprised most people in the health care community. Although the reports estimates of more than a million injuries and nearly 100000 deaths attributable to medical errors annually were based on figures from a study published in 1991 they were news to many. The mortality figures in particular have been a matter of some public debate23 although most agree that whatever the number of deaths is it is too high.
The report galvanized an enormous reaction from both government and health care representatives. Within two weeks Congress began hearings and the President ordered a government-wide feasibility study followed in February by a directive to governmental agencies to implement the IOM recommendations. During this time professional societies and health care organizations have begun to re-assess their efforts in patient safety.
The IOM report made four major points??the extent of harm that results from medical errors is great; errors result from system failures not people failures; achieving acceptable levels of patient safety will require major systems changes; and a concerted national effort is needed to improve patient safety. The national effort recommended by the IOM involves all stakeholders??professionals health care organizations regulators professional societies and purchasers. Health care organizations are called on to work with their professionals to implement known safe practices and set up meaningful safety programs in their institutions including blame-free reporting and analysis of serious errors. External organizations??regulators professional societies and purchasers??are called on to help establish standards and best practice for safety and to hold health care organizations accountable for implementing them.
Some of the best available data on the epidemiology of medical injury come from the Harvard Medical Practice Study.4 In that study drug complications were the most common adverse event (19 percent) followed by wound infections (14 percent) and technical complications (13 percent). Nearly half the events were associated with an operation. Most work on prevention to date has focused on adverse drug events and wound infections. Compared with the data on inpatients relatively few data on errors and injuries outside the hospital are available although errors in follow-up5 and diagnosis are probably especially important in non-hospital settings.
While the IOM report and Harvard Medical Practice Study deal primarily with injuries associated with errors in health care the costs of inefficiencies related to errors that do not result in injury are also great. One example is the effort associated with missed dose medication errors when a medication dose is not available for a nurse to administer and a delay of at least two hours occurs or the dose is not given at all.6 Nurses spend a great deal of time tracking down such medications. Although such costs are harder to assess than the costs of injuries they may be even greater.
Scope of Discussion
In this paper we are discussing only clear-cut errors in medical care and not suboptimal practice (such as failure to follow a guideline). Clearly this is not a dichotomous distinction and some examples may be helpful. We would consider a sponge left in the patient after surgery an error whereas an inappropriate indication for surgery would be suboptimal practice. We would consider it an error if no postoperative anticoagulation were used in patients in whom its benefit has clearly been demonstrated (for example patients who have just had hip surgery). However we would not consider it an error if a physician failed to follow a pneumonia guideline and prescribed a commonly used but suboptimal antibiotic even though adherence to such guidelines will almost certainly improve outcomes. Although we believe that information technology can play a major role in both domains we are not addressing suboptimal practice in this discussion.
Theory of Error
Although human error in health care systems has only recently received great attention human factors engineering has been concerned with error for several decades. Following the accident at Three Mile Island in the late 1970s the nuclear power industry was particularly interested in human error as part of human factors concerns and has produced a number of reports on the subject.7 The U.S. commercial aviation sector is also very interested in human error at present because of massive overhaul of the air traffic control network. A few excellent books on human error generally are available.8??10
While it is easy and common to blame operators for accidents investigation often indicates that an operator erred because the system was poorly designed. Testimony of an operator of the Three Mile Island nuclear power plant in a 1979 Congressional hearing11 makes the point If you go beyond what the designers think might happen then the indications are insufficient and they may lead you to make the wrong inferences.[H]ardly any of the measurements that we have are direct indications of what is going on in the system.
The consensus among man??machine system engineers is that we should be designing our control rooms cockpits intensive care units and operating rooms so that they are more transparent??that is so that the operator can more easily see through the displays to the actual working system or what is going on. Situational awareness is the term used in the aviation sector. Often the operator is locked into the dilemma of selecting and slavishly following one or another written procedure each based on an anticipated causality. The operator may not be sure what procedure if any fits the current not-yet-understood situation.
Machines can also produce errors. It is commonly appreciated that humans and machines are rather different and that the combination of both thus has greater potential reliability than either alone. However it is not commonly understood how best to make this synthesis. Humans are erratic and err in surprising and unexpected ways. Yet they are also resourceful and inventive and they can recover from both their own and the equipments errors in creative ways. In comparison machines are more dependable which means they are dependably stupid when a minor behavior change would prevent a failure in a neighboring component from propagating. The intelligent machine can be made to adjust to an identified variable whose importance and relation to other variables are sufficiently well understood. The intelligent human operator still has usefulness however for he or she can respond to what at the design stage may be termed an unknown unknown (a variable which was never anticipated so that there was never any basis for equations to predict it or computers and software to control it).
Finally we seek to reduce the undesirable consequences of error not error itself. Senders and Moray10 provide some relevant comments that relate to information technology: The less often errors occur the less likely we are to expect them and the more we come to believe that they cannot happen. It is something of a paradox that the more errors we make the better we will be able to deal with them. They comment further that eliminating errors locally may not improve a system and might cause worse errors elsewhere.
A medical example relating to these issues comes from the work of Macklis et al. in radiation therapy12; this group has used and evaluated the safety record of a record-and-verify linear accelerator system that double-checks radiation treatments. This system has an error rate of only 0.18 percent with all detected errors being of low severity. However 15 percent of the errors that did occur related to use of the system primarily because when an error in the checking system occurred the human operators assumed the machine had to be right even in the face of important conflicting data. Thus the Macklis group expressed concern that over-reliance on the system could result in an accident. This example illustrates why it will be vital to measure to determine how systems changes affect the overall rate of not only errors but accidents.
System Improvement and Error Improvement
Although the traditional approach in medicine has been to identify the persons making the errors and punish them in some way it has become increasingly clear that it is more productive to focus on the systems by which care is provided.13 If these systems could be set up in ways that would both make errors less likely and catch those that do occur safety might be substantially improved.
A system analysis of a large series of serious medication errors (those that either might have or did cause harm)13 identified 16 major types of system failures associated with these errors. Of these system failures all of the top eight could have been addressed by better medical information.
Currently the clinical systems in routine use in health care in the United States leave a great deal to be desired. The health care industry spends less on information technology than do most other information-intensive industries; in part as a result the dream of system integration been realized in few organizations. For example laboratory systems do not communicate directly with pharmacy systems. Even within medication systems electronic links between parts of the system??prescribing dispensing and administering??typically do not exist today. Nonetheless real and difficult issues are present in the implementation of information technology in health care and simply writing a large check does not mean that an organization will necessarily get an outstanding information system as many organizations have learned to their chagrin.
Evaluation is also an important issue. Data on the effects of information technology on error and adverse event rates are remarkably sparse and many more such studies are needed. Although such evaluations are challenging tools to assess the frequency of errors and adverse events in a number of domains are now available.14??19 Errors are much more frequent than actual adverse events (for medication errors for example the ratio in one study6 was 100:1). As a result it is attractive from the sample size perspective to track error rates although it is important to recognize that errors vary substantially in their likelihood of causing injury.20
Clinical Decision Support
While many errors can be detected and corrected by use of human knowledge and inspection these represent weak error reduction strategies. In 1995 Leape et al.13 demonstrated that almost half of all medication errors were intimately linked with insufficient information about the patient and drug. Similarly when people are asked to detect errors by inspection they routinely miss many.21
It has recently been demonstrated that computerized physician order entry systems that incorporate clinical decision support can substantially reduce medication error rates as well as improve the quality and efficiency of medication use. In 1998 Bates et al.20 found in a controlled trial that computerized physician order entry systems resulted in a 55 percent reduction in serious medication errors. In another time series study22 this group found an 83 percent reduction in the overall medication error rate and a 64 percent reduction even with a simple system. Evans et al.23 have also demonstrated that clinical decision support can result in major improvements in rates of antibiotic-associated adverse drug events and can decrease costs. Classen et al.24 have also demonstrated in a series of studies that nosocomial infection rates can be reduced using decision support.
Another class of clinical decision support is computerized alerting systems which can notify physicians about problems that occur asynchronously. A growing body of evidence suggests that such systems may decrease error rates and improve therapy thereby improving outcomes including survival the length of time patients spend in dangerous conditions hospital length of stay and costs.25??27 While an increasing number of clinical information systems contain data worthy of generating an alert message delivering the message to caregivers in a timely way has been problematic. For example Kuperman et al.28 documented significant delays in treatment even when critical laboratory results were phoned to caregivers. Computer-generated terminal messages e-mail and even flashing lights on hospital wards have been tried.29??32 A new system which transmits real-time alert messages to clinicians carrying alphanumeric pagers or cell phones promises to eliminate the delivery problem.3334 It is now possible to integrate laboratory medication and physiologic data alerts into a comprehensive real-time wireless alerting system.
Shabot et al.3334 have developed such a comprehensive system for patients in intensive care units. A software system detect alerts and then sends them to caregivers. The alert detection system monitors data flowing into a clinical information system. The detector contains a rules engine to determine when alerts have occurred.
For some kinds of alert detection prior or related data are needed. When the necessary data have been collected alerting algorithms are executed and a decision is made as to whether an alert has occurred (Figure 1 ). The three major forms of critical event detection are critical laboratory alerts physiologic exception condition alerts and medication alerts. When an alert condition is detected an application formats a message and transmits it to the alphanumeric pagers of various recipients on the basis of a table of recipients by message type patient service type and call schedule. The message is sent as an e-mail to the coded PIN (personal identification number) of individual caregivers pagers or cell phones. The message then appears on the devices screen and includes appropriate patient identification information (Figure 2 ).
Figure 1 Alert detection system. Three major forms of critical event detection occur??critical laboratory alerts physiologic exception condition alerts and medication alerts.
Figure 2 Wireless alerting system. In the Cedars-Sinai system alerts are initially detected by the clinical system then sent to a server then via the Internet then sent over a PageNet transmitter to a two-way wireless device.
Alerts are a crucial part of a clinical decision support system35 and their value has been demonstrated in controlled trials.2735 In one study Rind et al.27 alerted physicians via e-mail to increases in serum creatinine in patients receiving nephrotoxic medications or renally excreted drugs. Rind et al. reported that when e-mail alerts were delivered medications were adjusted or discontinued an average of 21.6 hours earlier than when no e-mail alerts were delivered. In another study Kuperman et al.35 found that when clinicians were paged about panic laboratory values time to therapy decreased 11 percent and mean time to resolution of an abnormality was 29 percent shorter.
As more and different kinds of clinical data become available electronically the ability to perform more sophisticated alerts and other types of decision support will grow. For example medication-related laboratory physiologic data can be combined to create a variety of automated alerts. (Table 1 shows a sample of those currently included in the system used at Cedars-Sinai Medical Center Los Angeles California.) Furthermore computerization offers many tools for decision support but because of space limitations we have discussed only some of these; Among the others are algorithms guidelines order sets trend monitors and co-sign forcers. Most sophisticated systems include an array of these tools.
Errors Generated by Information Technology
Although information technology can help reduce error and accident rates it can also cause errors. For example if two medications that are spelled similarly are displayed next to each other substitution errors can occur. Also clinicians may write an order in the wrong patients record.
In particular early adopters of vendor-developed order entry have reported significant barriers to successful implementation new sources of error and major infrastructure changes that have been necessary to accommodate the technology. The order entry process with many computerized physician order entry systems currently on the market is error-prone and time-consuming. As a result prescribers may bypass the order entry process totally and encourage nurses pharmacists or unit secretaries to enter written or verbal drug orders. Also most computerized physician order entry systems are separate from the pharmacy system which requires double entry of all orders. This may result in electronic/computer-generated medication administration records (MARs) that are derived from the order entry system database not the pharmacy database which can result in discrepancies and extra work for nurses and pharmacists.
Furthermore many computerized physician order entry systems lack even basic screening capabilities to alert practitioners to unsafe orders relating to overly high doses allergies and drug??drug interactions. While visiting hospitals in 1998 representatives of the Institute for Safe Medication Practices (ISMP) tested pharmacy computers and were alarmed to discover that many failed to detect unsafe drug orders. Subsequently ISMP asked directors of pharmacy in U.S. hospitals to perform a nationwide field test to assess the capability of their systems to intercept common or serious prescribing errors.36 To participate pharmacists set up a test patient in their computer system then entered actual
physician prescription errors that had actually led to a patients death or serious injury during 1998 (Table 2 ). Only a small number of even fatal errors were detected by current detection methods.
Table 2 Percentage of Pharmacy Computer Systems That Failed to Provide Unsafe Order Alerts*
Unsafe Order Not Detected
Can Override Without Note
Cephradine oral suspension IV
Ketorolac 60 mg IV (patient allergic to aspirin)
Vincristine 3 mg IV x 1 dose (2-year-old)
Colchicine 10 mg IV for 1 dose (adult)
Cisplatin 204 mg IV x 1 dose (26-kg child)
* All these orders are unsafe and have resulted in at least one fatality in the United States. However most pharmacy systems did not detect them and even among those that did a large percentage allowed an override without a note. Data reprinted with permission from ISMP Medication Safety Alert! Feb 10 1999.36 Copyright ? Institute for Safe Medication Practices.
These anecdotal data suggest that current systems may be inadequate and that simply implementing the current off-the-shelf vendor products may not have the same effect on medication errors that has been reported in research studies. Improvement of vendor-based systems and evaluation of their effects is crucial since these are the systems that will be implemented industry-wide.
A major problem in creating the will to reduce errors has been that administrators have not been aware of the magnitude of problem. For example one survey showed that while 92 percent of hospital CEOs reported that they were knowledgeable about the frequency of medication errors in their facility only 8 percent said they had more than 20 per month when in fact all probably had more than this.37 Probably in part as a result the Advisory Board Company found that reducing clinical error and adverse events ranked 133rd when CEOs were asked to rank items on a priority list.38 A number of efforts are currently under way to increase the visibility of the issue. For example a video about this issue which was developed by the American Hospital Association and the Institute for Healthcare Improvement has been sent to all hospital CEOs in the United States and a number of indicators suggest that such efforts may be working.
For information technology to be implemented it must be clear that the return on investment is sufficient and far too few data are available regarding this in health care. Furthermore there are many horror stories of huge investments in information technology that have come to naught.
Positive examples relate to computer order entry. At one large academic hospital the savings were estimated to be $5 million to $10 million annually on a $500 million budget.39 Another community hospital predicts even larger savings with expected annual savings of $21 million to $26 million representing about a tenth of its budget.40 In addition in a randomized controlled trial order entry was found to result in a 12.7 percent decrease in total charges and a 0.9 day decrease in length of stay.41 Even without full computerization of ordering substantial savings can be realized: data from LDS Hospital23 demonstrated that a program that assisted with antibiotic management resulted in a fivefold decrease in the frequency of excess drug dosages and a tenfold decrease in antibiotic-susceptibility mismatches with substantially lower total costs and lengths of stay.
Despite these demonstrated benefits only a handful of organizations have successfully implemented clinical decision support systems. A number of barriers have prevented implementation. Among these are the tendency of health care organizations to invest in administrative rather than clinical systems; the issue of silo accounting so that benefits that accrue across a system do not show up in one budget and thus do not get credit; the current financial crisis in health care which has been exacerbated by the Balanced Budget Amendment and has made it very hard for hospitals to invest; the lack at many sites of leaders in information technology; and the lack of expertise in implementing systems.
One of the greatest barriers to providing outstanding decision support however has been the need for an extensive electronic medical record system infrastructure. Although much of the data required to implement significant clinical decision support is already available in electronic form at many institutions the data are either not accessible or cannot be brought together to be used in clinical decision support because of format and interface issues. Existing and evolving standards for exchange of information (HL7) and coding of this data are simplifying this task. Correct and consistent identification of patients doctors and locations is another area in which standards are needed. Approaches to choosing which information should be coded and how to record a mixture of structured coded information and unstructured text are still immature.
Some organizations have moved ahead with adopting such standards on their own and this can have great benefits. For example a technology architecture guide was developed at Cedars-Sinai Medical Center to help ensure that its internal systems and databases operate in a coherent manner. This has allowed them to develop what they call their Web viewing system which allows clinicians to see nearly all results on an Internet platform. Many health care organizations are hamstrung because they have implemented so many different technologies and databases that information stays in silos.
A second major hurdle is choosing the appropriate rules or guidelines to implement. Many organizations have not developed processes for developing and implementing consensus choices in their physician groups. Once the focus has been determined the organization must determine exactly what should be done about the selected problem. Regulatory and legal issues have also prevented vendors from providing this type of content. Finally despite good precedents for delivering feedback to clinicians for simple decision support changing provider behavior for more complex aspects of care remains challenging.
A national commitment to safer health care is developing. Although it is too soon to determine how it will play out (the initial fixation on mandatory reporting has been an unwelcome diversion for example) it seems clear that many stakeholders have a real interest in improving safety. Doctors and other professionals are in the interesting position of being expected to be both leaders in this movement and the recipients of its attention. Already a national coalition involving many of
NB: We do not resell papers. Upon ordering, we do an original paper exclusively for you.
The post Frequency of Errors in Medicine Using Information Technology Literature strongly suggests that the f appeared first on Student Homeworks.