Why Is Data Accuracy Important In Healthcare?

Council Post: The Accuracy Limits Of Data-Driven Healthcare PhD, MBA, CTO at, Making AI & NLP solve real-world problems in healthcare, life science and related fields. getty Algorithms are only as good as the quality of data they’re being fed. This is not a new concept, but as we begin to rely more heavily on data-driven technologies, such as artificial intelligence (AI) and other automation tools and applications, it’s becoming a more important one.

• Recent from MIT found a high number of errors in publicly available datasets that are widely used for training models.
• An average of 3.3% errors were found in the test sets of 10 of the most widely used computer vision, natural language processing (NLP) and audio datasets.
• Given that accuracy baselines are often at or above 90%, this means that a lot of research innovation amounts to chance — or overfitting to errors.

Data science practitioners should exercise caution when choosing which models to deploy based on small accuracy gains on such datasets. These findings are particularly concerning when it comes to AI applications in high-stakes industries like healthcare.

• Outcomes in this field have the ability to prevent disease, accelerate the development of life-saving medicine and help us understand the spread of disease and other critical health trends.
• While accuracy in healthcare is vital to success, it’s also rife with complexities that make this extremely challenging.

One of the reasons for this is the data source. More than half of the clinically relevant data for applications like recommending a course of treatment, finding actionable genomic biomarkers or matching patients to clinical trials is only found in free-text.

1. This includes physicians notes, diagnostic imaging, pathology reports, lab reports and other sources not available as structured data within electronic health records (EHR).
2. These information sources include nuances and data quality issues that make it hard to connect the dots and get a full picture of a patient.

Another barrier exists in the limitations of what’s in the data itself. Because there are no shared standards for data collection across hospitals and healthcare systems, inconsistencies and inaccuracies are common. Between different organizations collecting different information and records not being updated on a consistent basis, it’s difficult to know how accurate the data is — especially if it’s being moved and updated among different providers.

It’s not just providers to blame, either — inaccuracies come directly from the patients themselves. A from The Journal of General Internal Medicine shows just how prevalent this can be. When exploring the accuracy of race, ethnicity and language preference in EHRs, the study found that 30% of whites self-reported identification with at least one other racial or ethnic group, as did 37% of Hispanics and 41% of African Americans.

Patients were also less likely to complete the survey in Spanish than the language preference noted in the EHR would have suggested. There’s clearly a need for better data collection practices in healthcare and beyond. Accurate information can help the medical community understand more about social determinants of health, patient risk prediction, clinical trial matching and more.

1. Standardizing how this data is collected and recorded can ensure the clean data gets shared and analyzed correctly.
2. This is both a medical and social challenge.
3. For example, what is the “correct” race to fill in? When exactly is someone considered a smoker? This is also partly a technology challenge, as we’re already way beyond the limit of what’s reasonable to ask providers and patients to manually input.

There are also data quality issues outside our direct control, such as fraud and abuse. The National Health Care Anti-Fraud Association that “healthcare fraud costs the nation about $68 billion annually — about 3% of the nation’s $2.26 trillion in healthcare spending.

1. Other estimates range as high as 10% of annual healthcare expenditure, or $230 billion.” While we can account for error rates within the data, it’s an imperfect science at the end of the day, and it’s important to understand its limitations.
2. That said, it’s not all doom and gloom when it comes to quality data or the algorithms we use.

Technology that can automatically understand the nuances of unstructured text and images, as well as reconcile conflicting and missing data points, is gradually maturing. NLP, for example, can address many pitfalls of data quality, such as uncovering disparities in an EHR versus a doctor’s transcript or what a patient self-reports.

1. In recent years, newer algorithms and models can apply the context, medium and intent of each data source to infer useful semantic answers.
2. This is especially useful when you consider how specific clinical language is.
3. Take how we indicate triple-negative breast cancer (TNBC), for instance.
4. While the acronym TNCB isn’t hard to identify, the condition can also be denoted as Er-/pr-/h2-, (er pr her2) negative, tested negative for the following: er, pr, h2 and triple negative neoplasm of the upper left breast, to name a few.

NLP can identify variations of these terms when they are in context — and healthcare-specific deep learning models have gotten very good at this. Current state-of-the-art, peer-reviewed, publicly reproducible accuracy benchmarks on both competitive academic benchmarks and real-world production deployments has been steadily improving over the last five years.

1. Libraries like Spark NLP surpass 90% accuracy on a variety of clinical and biomedical text understanding tasks.
2. Reproducibility of results, consistency of applying clinical guidelines at scale and the ability to easily tune models to a specific clinical use case or setting are three keys to successful implementations and to building broader trust in AI technology.

The healthcare industry is varied and complex and so, too, is the information collected. When using data to make any decision in this field, technology that helps will keep improving. But it’s critical to remember the fundamental limitations of data quality and accuracy that power these algorithms.

What is the importance of accuracy in the medical field?

Most medical practices, in particular large ones, have to keep track of a lot of information. Within each patient is a comprehensive medical history, also known as their record. It gives medical providers—quite literally—up-to-the-minute information on a patient’s condition.

• Yet, within these records is a lot of room for error.
• Multiple people can make changes.
• Conditions might go undiagnosed.
• Mistakes can happen.
• Improper reporting, inaccuracies and omissions could lead to actions that harm a patient.
• When patients experience harm, they might have grounds to sue for malpractice.

The Importance of Accurate Medical Records When medical mistakes occur, most people want to figure out why they happened. One of the first places they will usually turn is to the medical record. Because of their detailed information, most records can help pinpoint where mistakes occurred.

1. In effect, they can help provide patients with better care.
2. After malpractice claims, accurate records might even help settle the claim.
3. Nevertheless, inaccurate records might prove liabilities.
4. Should improper recording lead to mistakes, you might find yourself facing a lawsuit.
5. Therefore, your commitment should be to enforce proper record-keeping practices at all times.

Recordkeeping Best Practices Every medical office likely has its own process for collecting medical records. There are always principles that each practice should enshrine into its ethical standards.

Monitor the security of all records systems. If you keep paper records, these should remain strictly under lock and key. Your electronic records must have proper cybersecurity protection. All those who have access to records must have the proper credentials. Privacy law governs the disclosure of medical records. Ensure that you never release records unless under lawful pretenses. The practice should adapt a multi-step approval process regarding the release of records. Always remember that detail is important in medical records. Physicians should rely on their own observation, the observations of other providers, and the observations of the patients to create comprehensive pictures of the person. Most physicians and providers receive comprehensive training on how to accurately report information. Keep in mind, doctors may update records. However, they should not attempt to hide or alter information otherwise proven inaccurate. Falsifying records is an extreme breach of medical ethics. Therefore, it does not qualify for medical malpractice insurance coverage.

Conscientious medical providers should institute strict recordkeeping practices. These should reflect established and required industry standards. However, they should also develop, where appropriate, individualized approaches to record keeping. Your goal, at the end of the day, should be to provide the most comprehensive pictures of your patients.

Why is accurate reliable and timely data important in healthcare?

Why is data quality important in healthcare? – The importance of data quality in healthcare stems from how data can improve patient care. In NHS England’s own words, “Consistent, timely and accurate data improves patient care and decision making”, The opposite can be said to be true as well: the combined impact of problems ultimately leads to poor care, poor care planning, and poor policy decisions.

1. Poor data means inappropriate care for a patient, which can have a knock-on effect.
2. The obvious impact is that a patient’s health would be damaged, but there’s also the impact of wasted time and expense.
3. Giving a patient the wrong treatment requires further treatment to correct the mistake.
4. This can take valuable time and resources away from other patients, limiting the scope of an organisation’s ability to care.

Healthcare professionals need to have confidence in the information provided to them. Better quality data empowers doctors and nurses, and this gives them confidence in their decision making. By feeling supported there is also a boost in morale and job satisfaction for healthcare professionals, so an organisation loses fewer staff to stress or burnout, which in turn brings down turnover and boosts productivity: a win-win situation.

What is data accuracy?

What is data accuracy? – Data accuracy, as the essential standard of data quality, refers to the consistency of data with reality. Because more conformity means more accuracy, so the accurate data must reflect the information you require. This also means that the data is error-free and has a reliable and consistent source of information.

1. Therefore, even though it may not be possible to get 100% truth, you should target to reach the optimum.
2. Accurate data is substantial for forecasting, planning, program budgeting, strategy development, and any business operation.
3. Data accuracy also includes totality, validity, and consistency.
4. Your goals, projects, and projection for the future may fail if the data is inaccurate, incomplete, and unreliable.

It can cause you to make wrong business decisions at critical junctures. For instance, according to a study, 70% of data managers believe that inaccurate predictions are a hazard to their and the company’s reputation. And inaccurate predictions are usually rooted in inaccurate data.

Why is data quality important?

Why is data quality important? – Data quality is essential for one main reason: You give customers the best experience when you make decisions using accurate data. A great customer experience leads to happy customers, brand loyalty, and higher revenue for your business.

If you’re using poor-quality data, you’re mostly guessing at what your customers want. Worse still, you might be actively doing things your customers dislike. Collecting trustworthy data and updating existing records gives you a better understanding of your customers. It also lets you keep in contact with them using verified email addresses, mailing information, and phone numbers.

This information helps you market effectively and use resources efficiently. Maintaining data quality can help you stay ahead of your competitors, too. Reliable data keeps your business agile. You can spot trends and industry changes sooner so you can take advantage of new opportunities or tackle challenges before your competitors.

What is data precision in healthcare?

Abstract – Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments.

• It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers).
• As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times.

Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data.

Besides, the public, who is the data source, always expects the security, privacy, and trust of their data. Otherwise, they can avoid contributing their data to the precision health system. Consequently, as the public is the targeted beneficiary of the system, the effectiveness of precision health diminishes.

Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs.

1. Then it presents the requirements and investigates the associated challenges.
2. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects.
3. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.

Keywords: Artificial intelligence; Ethical guidelines; Legal requirements; Precision health; Privacy; Security. Copyright © 2020 Elsevier Ltd. All rights reserved.

What does accuracy mean in healthcare?

Introduction: – Emergency physicians, like other specialists, are faced with different patients and various situations every day. They have to use ancillary diagnostic tools like laboratory tests and imaging studies to be able to manage them ( 1 – 8 ).

In most cases, numerous tests are available. Tests with the least error and the most accuracy are more desirable. The power of a test to separate patients from healthy people determines its accuracy and diagnostic value ( 9 ). Therefore, a test with 100% accuracy should be the first choice. This does not happen in reality as the accuracy of a test varies for different diseases and in different situations.

For example, the value of D-dimer for diagnosing pulmonary embolism varies based on pre-test probability. It shows high accuracy in low risk patient and low accuracy in high risk ones. The characteristics of a test that reflects the aforementioned abilities are accuracy, sensitivity, specificity, positive and negative predictive values and positive and negative likelihood ratios ( 9 – 11 ).

In this educational review, we will simply define and calculate the accuracy, sensitivity, and specificity of a hypothetical test. Definitions: Patient: positive for disease Healthy: negative for disease True positive (TP) = the number of cases correctly identified as patient False positive (FP) = the number of cases incorrectly identified as patient True negative (TN) = the number of cases correctly identified as healthy False negative (FN) = the number of cases incorrectly identified as healthy Accuracy: The accuracy of a test is its ability to differentiate the patient and healthy cases correctly.

To estimate the accuracy of a test, we should calculate the proportion of true positive and true negative in all evaluated cases. Mathematically, this can be stated as: Accuracy = TP + TN TP + TN + FP + FN Sensitivity: The sensitivity of a test is its ability to determine the patient cases correctly.

To estimate it, we should calculate the proportion of true positive in patient cases. Mathematically, this can be stated as: Sensitivity = TP TP + FN Specificity: The specificity of a test is its ability to determine the healthy cases correctly. To estimate it, we should calculate the proportion of true negative in healthy cases.

Mathematically, this can be stated as: Specificity = TN TN + FP Examples: Scenario 1 Imagine we have a sample of 100 cases, 50 healthy and the others patient. If a test can be positive for all patients and be negative for all the healthy ones, it is 100% accurate. A schematic presentation of an example test with 100% accuracy, sensitivity, and specificity Taking into account the mentioned statistical characteristics, this test is appropriate for both screening and final verification of a disease. Scenario 2 If the test can only diagnose 25 out of the 50 patients and has reported the others as healthy ( Figure 2 ); accuracy, sensitivity, and specificity will be as follows: A schematic presentation of an example test with 75% accuracy, 50% sensitivity, and 100% specificity. Accuracy: Of the 100 cases that have been tested, the test could determine 25 patients and 50 healthy cases correctly. Therefore, the accuracy of the test is equal to 75 divided by 100 or 75%.

Sensitivity: From the 50 patients, the test has only diagnosed 25. Therefore, its sensitivity is 25 divided by 50 or 50%. Specificity: From the 50 healthy people, the test has correctly pointed out all 50. Therefore, its specificity is 50 divided by 50 or 100%. According to these statistical characteristics, this test is not suitable for screening purposes; but it is suited for the final confirmation of a disease.

Scenario 3 This time we will assume that the test has been able to identify 25 of the 50 healthy cases and has reported the others as patients ( Figure 3 ). In this scenario accuracy, sensitivity and specificity will be as follows: A schematic presentation of an example test with 75% accuracy, 100% sensitivity, and 50% specificity. Accuracy: Of the 100 cases that have been tested, the test could identify 25 healthy cases and 50 patients correctly. Therefore, the accuracy of the test is equal to 75 divided by 100 or 75%.

Why is it important to perform with accuracy?

General Importance of Accuracy at Work – To be accurate and precise at work helps a company grow, profit, and function efficiently. Accuracy can also help a company know its budget, employee expenses, and projections for revenue. Whether you’re in the construction industry, an accounting professional, or a healthcare provider, accuracy in the workplace is crucial.

But, beyond being accurate at the actual job at hand, properly tracking time invested into your work is just as important. Having a clear understanding of where and how employees’ time is spent allows employers to know if their business is operating to its full potential. Additionally, organizations worldwide will improve their image and brand when it comes to accurate work performance.

Finally, precision plays a considerable role in how the community views your ethics and quality of work. For example, if your company is providing inaccurate billing information, some could perceive that your company is being unethical, unprofessional, or not trustworthy.

What is the importance of ensuring accurate and appropriate data collection methods?

What is Data Collection: A Definition – Before we define what is data collection, it’s essential to ask the question, ” What is data ?” The abridged answer is, data is various kinds of information formatted in a particular way. Therefore, data collection is the process of gathering, measuring, and analyzing accurate data from a variety of relevant sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities.

Our society is highly dependent on data, which underscores the importance of collecting it. Accurate data collection is necessary to make informed business decisions, ensure quality assurance, and keep research integrity. During data collection, the researchers must identify the data types, the sources of data, and what methods are being used.

We will soon see that there are many different data collection methods, There is heavy reliance on data collection in research, commercial, and government fields. Before an analyst begins collecting data, they must answer three questions first:

What’s the goal or purpose of this research? What kinds of data are they planning on gathering? What methods and procedures will be used to collect, store, and process the information?

Additionally, we can break up data into qualitative and quantitative types. Qualitative data covers descriptions such as color, size, quality, and appearance. Quantitative data, unsurprisingly, deals with numbers, such as statistics, poll numbers, percentages, etc.

What is good data accuracy?

What Is Data Accuracy? – Data accuracy is, as its sounds, whether or not given values are correct and consistent. The two most important characteristics of this are form and content, and a data set must be correct in both fields to be accurate. For example, imagine a database containing information on employees’ birthdays, and one worker’s birthday is January 5th, 1996.U.S.

• Formats would record that as 1/5/1996, but if this employee is European, they may record it as 5/1/1996.
• This difference could cause the database to incorrectly state that the worker’s birthday is May 1, 1996.
• In this example, while the data’s content was correct, its form wasn’t, so it wasn’t accurate in the end.

If information is of any use to a company, it must be accurate in both form and content.

How important is data accuracy and integrity?

Importance of Data accuracy and Data integrity – Any company needs data accuracy and integrity to help ensure that the data is comprehensive, consistent, and accurate. Data accuracy is important for a business because it provides correct and up-to-date information.

What would happen if your data is inaccurate?

Lost revenue – Poor-quality data can lead to lost revenue in many ways. Take, for example, communications that fail to convert to sales because the underlying customer data is incorrect. Poor data can result in inaccurate targeting and communications, especially detrimental in multichannel selling,

What are the benefits of improved data quality?

What is data quality? – Data quality is a measurement of company data that looks at metrics such as consistency, reliability, completeness and accuracy. The highest levels of data quality are achieved when data is accessible and relevant to what business users are working on.

• To use a simple baking analogy, high data quality is achieved with the right ingredients (data cleaning), measured correctly ( data preparation ) and combined in the right way (data transformation) to create a delicious end-product (actionable insights).
• The benefits of data quality are numerous and impactful.

Good data allows businesses to make better decisions, improve operational efficiencies, optimize marketing campaigns and boost customer satisfaction. It is essential for businesses that want to develop a competitive edge. SEE: Hiring Kit: Database Engineer (TechRepublic Premium) When data is accurate, complete and consistent, organizations can make informed decisions that lead to positive outcomes.

What is data accuracy vs precision?

Accuracy refers to how close a measurement is to the true or accepted value. Precision refers to how close measurements of the same item are to each other. Precision is independent of accuracy.

What is the difference between precision and accuracy healthcare?

ACCURACY AND PRECISION – Accuracy and precision are important terms in CO monitoring. Accuracy refers to the ability to measure or derive the actual SV/CO, whereas precision refers to the reproducibility of a measurement. The ODM measures blood flow velocity in the descending thoracic aorta. Numerous studies have compared CO measurements from the ODM with those from the pulmonary artery catheter (PAC), with good overall agreement (see for systematic review). However, there are limitations in comparing new technologies to the PAC. Despite being deemed the clinical ‘gold standard’ in CO measurement, the PAC has an error of ±20%,

1. As a result this makes it difficult to understand the true accuracy of newer technologies that are calibrated against and/or compared to the PAC.
2. Because of these limitations in the accurate measurement of CO, the precision or reproducibility of the technology becomes important.
3. ​ The precision of a technology can be assessed by taking repeated measurements of SV or CO in a haemodynamically stable patient over a short period or time.

The variability of the data set can then be calculated. This is most commonly reported as the coefficient of variation (the standard deviation (SD) as a percentage of the mean value). The figure below shows the clustering of normally distributed data around the mean value. This normal distribution of data enables the determination of confidence intervals. In CO monitoring terms this is the change in measured CO (or SV) required to be x% confident that the change is real and not due to inherent measurement error within the technology.

• Based on the above If the SV/CO changes by 1 SD, the user can be ~70% sure that the change is real.
• If the SV/CO changes by 2 SD, the user can be ~95% sure that the change is real.
• If the SV/CO changes by 2.5 SD, the user can be ~99% sure that the change is real.
• In order to measure whether a variable has changed, the ‘amount’ of measured change must be greater than the precision of the technology.

If the amount of change is 2.5 times the precision, the user can be 99% sure they have measured a real change in the variable. But, what are the values of ‘1 SD’ (or coefficient of variation) and therefore the 99% confidence intervals for each of the different CO measurement technologiesi.e., how precise are they? ​ The table below summarises the available published precision data on the different CO monitoring technologies. PAC, pulmonary artery catheter; ODM, oesophageal Doppler monitor; PPWA, pulse pressure waveform analysis. The precision of a technology dictates its ability to guide fluid management. The 10% SV optimisation algorithm used to optimise SV is specific to the oesophageal Doppler device, and is evidence-based.

What are the 5 traits of data quality?

Timeliness – Timeliness, as the name implies, refers to how up to date information is. If it was gathered in the past hour, then it’s timely – unless new information has come in that renders previous information useless. The timeliness of information is an important data quality characteristic, because information that isn’t timely can lead to people making the wrong decisions.

In turn, that costs organizations time, money, and reputational damage. “Timeliness is an important data quality characteristic – out-of-date information costs companies time and money” In today’s business environment, data quality characteristics ensure that you get the most out of your information.

When your information doesn’t meet these standards, it isn’t valuable. Precisely provides to improve the accuracy, completeness, reliability, relevance, and timeliness of your data. Find out more in our eBook:

What is an example of accuracy data quality?

Data Quality Dimension #4: Accuracy – Accuracy is the degree to which data correctly reflects the real world object OR an event being described. Examples:

Sales of the business unit are the real value. Address of an employee in the employee database is the real address.

Questions you can ask yourself: Do data objects accurately represent the “real world” values they are expected to model? Are there incorrect spellings of product or person names, addresses, and even untimely or not current data? These issues can impact operational and advanced analytics applications.

What is accuracy and why is it important?

Accuracy – Information Ethics and Security Accuracy is to be ensuring that the information is correct and without any mistake. Information accuracy is important because may the life of people depend in it like the medical information at the hospitals, so the information must be accurate.

The quality of information measured by accuracy, timeliness, completeness, relevance and if it is easy to understood by the users, so the accuracy important for quality of information. And the accuracy represents all organization actions. To get accurate information we need the right value. If someone gave inaccurate information, it is difficult to find who made the mistake.

There are many reasons for inaccurate information. The most common case is when the user enter wrong value. Also inaccurate information may accrue by typographical mistake. To avoid this mistakes the organization must find who has experience and skills for data entry and it must use the programs which discover the typographical mistake.

What is accuracy and its importance?

The ability of an instrument to measure the accurate value is known as accuracy. In other words, it is the the closeness of the measured value to a standard or true value. Accuracy is obtained by taking small readings. The small reading reduces the error of the calculation.

Why is accuracy important in clinical trials?

Clinical trials need accurate and updated patient coding systems. Without the latest codes and advanced technology to check for errors, researchers risk presenting poor, inaccurate data that draws the wrong conclusions.

Why is accuracy important in nursing?

Margaret Lunney, RN, PhD Margaret Lunney is a Professor and Graduate Nursing Programs Coordinator at the College of Staten Island, the City University of the New York (CUNY), and Doctoral Faculty at CUNY’s Graduate Center. For 25 years, her research and professional activities have focused on community health nursing, critical thinking, and the concept of accuracy of nurses’ diagnoses, measurement of accuracy, and use of standardized nursing languages. Among the honors she received are the 2001 Distinguished Nurse Researcher Award from the New York State Nurses’ Foundation and a 2007 Fulbright Award as research consultant and lecturer in Japan. She received her M.S. in nursing from Hunter College, CUNY, and her PhD in Nursing Science from New York University.

Article

Abstract Studies published from 1966 to 2006 describe how nurses’ interpretations of clinical data vary widely, thus significant percentages of nurses’ diagnoses may be of low accuracy. This is important because data interpretations, or diagnoses, serve as the basis for selection of interventions and the subsequent achievement of patient outcomes.

• Accuracy of nurses’ diagnoses is defined as a rater’s judgment of the match between a diagnostic statement and patient data.
• Low accuracy can lead to wasted time and energy, harm to patients, absence of positive outcomes, and patient and family dissatisfaction.
• The purpose of this article is to appeal to nurses in both practice and education to address the accuracy of nurses’ diagnoses.

This appeal is based on three factors: (a) research evidence indicates the need for greater consistency among nurses in making nurses diagnoses, (b) accuracy of nurses’ diagnoses will always be an issue of concern because diagnosis in nursing is complex, and (c) with implementation of electronic health records, the degree of accuracy of nurses’ diagnoses will have broad-based implications.

In this article, the need for nurses to be accountable for addressing diagnostic accuracy is explained and strategies to improve accuracy related to the diagnostician, the diagnostic task, and the situational context are recommended. Some of these strategies include a greater focus on educational methods and content for development of nurses as diagnosticians, adoption of partnership models of nurse-patient relationships, an increase in opportunities for critical thinking and clinical decision making, selection of software with appropriate structures and content libraries, and a change in health care policies.

Key words: administration, clinical judgement, education, evidence-based practice, nursing diagnosis The research evidence is strong that it is time to address the accuracy of nurses’ diagnoses. Accuracy of nurses’ data interpretations (diagnoses) should be a serious concern of nurses in both practice and education because interpretations of patient data serve as the basis for selecting the nursing interventions that will achieve positive patient outcomes.

1. Accuracy of nurses’ diagnoses is defined as a rater’s judgment of the match between a diagnostic statement and patient data ( Lunney, 1990, 2001 ).
2. The research evidence is strong that it is time to address the accuracy of nurses’ diagnoses and consider strategies to improve accuracy.
3. In an analysis of 20 studies published from 1966 to 2000, Lunney ( 2001 ) reported that in all clinical simulation studies, and also in a study involving clinical cases, nurses’ interpretations of the same data varied widely.

Since 2000, investigators of nurses’ clinical reasoning and critical thinking abilities have also indicated that interpretations from the same data vary from nurse to nurse ( Brannon & Carson, 2003 ; Ebright, Patterson, Chalko, & Render, 2003 ; Ferrario, 2003 ; Hicks, Merritt, & Elstein, 2003 ; Junnola, Eriksson, Salantera, & Lauri, 2002 ; Puntillo, Neighbor, O’Neill, & Nixon, 2003 ; Redden & Wooten, 2001 ; Reischman & Yarandi, 2002 ).

When interpretations vary, some of the interpretations represent low accuracy. This is serious because low diagnostic accuracy contributes to harm to patients through: wasted time and energy, implementing ineffective interventions, absence of positive outcomes, and patient and family dissatisfaction.diagnosis in nursing is complex,low diagnostic accuracy contributes to harm to patients.

A concern about the accuracy of nurses’ diagnoses relates to all nursing care whether or not standardized nursing diagnoses, such as NANDA International ( NANDA-I, 2007 ), are used. Nursing interventions are based on data interpretations, whether or not nursing diagnoses are stated.

For example, even the act of a nurse deciding to help a patient move from a bed to a chair is based on the nurse’s interpretation of that patient’s data. Data interpretations are considered as diagnoses when they are complex enough to vary in accuracy and when they serve as the basis for interventions.

For example, interpreting a patient’s skin color as pale is not complex enough to serve as the basis for interventions. Instead, it is a data element that may contribute to making a nursing diagnosis, such as Acute Pain, Fear, or Fluid Volume Deficit.

• The purpose of this paper is to appeal for nurses in all settings to address the accuracy of nurses’ diagnoses of human responses.
• This appeal is based on three factors: (a) research evidence indicates the need for greater consistency among nurses in making nursing diagnoses, (b) accuracy of nurses’ diagnoses will always be an issue of concern because diagnosis in nursing is complex, and (c) with implementation of electronic health records, the degree of accuracy of nurses’ diagnoses will have broad-based implications.

Nurses’ accountability for accuracy is described and strategies to achieve accuracy of nurses’ diagnoses are presented. Appeal for Nurses in Practice and Education to Address the Accuracy of Nurses’ Diagnoses The need to address the accuracy of nurses’ diagnoses derives from three major factors.

1. First, there is sufficient research-based evidence in relation to the diagnostician, the nature of the diagnostic task, and situational contexts to establish that diagnostic accuracy varies widely.
2. Second, accuracy of nurses’ diagnoses will always be an issue of concern because the diagnosis of human responses is complex.

Third, implementation of electronic health records (EHRs) will result in the degree of accuracy having more significant effects on nursing care outcomes and the quality of nursing care than paper records have had. Research-Based Evidence that Accuracy Varies Research evidence supports that nurses’ interpretations of patient data vary widely.

• Hence the matter of diagnostic accuracy needs to be addressed.
• Carnevali and Thomas ( 1993 ) and Gordon ( 1994 ), theorists describing the diagnostic process in nursing, have identified the many factors influencing variations in accuracy.
• These factors have been classified as the: (a) diagnostician, (b) nature of the diagnostic task, and (c) situational context.

Research reports related to diagnostic accuracy in each of these areas will be discussed below. Diagnostician, Because nurses interpret patient data to make human response diagnoses, such as pain, they are considered diagnosticians. The characteristics of nurse diagnosticians, such as experience, education, and abilities in intellectual, interpersonal, and technical domains are important influencing factors in clinical decision making, including accuracy of diagnosing human responses.

1. In a clinical study, for example, only eight (12.9%) of 62 staff nurses in three hospitals achieved the highest accuracy score on a seven point scale of diagnostic accuracy when rated for accuracy by two clinical nurse experts who assessed the same patients ( Lunney, Karlik, Kiss & Murphy, 1997 ).
2. The research support is strong.in indicating that teaching nursing students about nursing diagnoses and the use of the diagnostic process is associated with higher accuracy.

Regarding the element of experience, it is not years of experience in nursing that influences accuracy of diagnosis, but rather the experience of working with the same types of patients ( Carnevali & Thomas, 1993 ; Gordon, 1994 ; Lunney, 2001 ). This is consistent with Benner’s ( 1984 ) findings related to the value of nursing experience, and with the findings of cognitive scientists that the critical thinking needed for clinical judgments is not a generic skill but one that is knowledge-specific ( Willingham, 2007 ).

• In addition, nursing experience can generate both good and bad habits of mind, habits known as heuristics.
• Good habits facilitate positive effects on accuracy whereas bad habits may decrease accuracy ( Brannon & Carson, 2003 ; Thompson, 2003 ).
• One heuristic that would have a negative effect on accuracy, for example, is overconfidence.

Overconfidence occurs with a belief that experience automatically provides excellent ability to interpret clinical data; it represents flawed self-assessment ( Gambrill, 2005 ). The research support is weak that higher educational levels are related to higher diagnostic accuracy ( Lunney, 2001 ).

The research support is strong, however, in indicating that teaching nursing students about nursing diagnoses and the use of the diagnostic process is associated with higher accuracy. Eight studies were reported to support this latter relationship ( Lunney, 2001 ). In relation to nurses’ abilities in the intellectual domain, a large number of studies have shown that critical thinking and clinical reasoning abilities vary widely resulting in variance in diagnostic ability (e.g., Brannon & Carson, 2003 ; Ebright, et al., 2003 ; Ferrario, 2003 ; Hicks, et al., 2003 ; Junnola, et al., 2002 ; Puntillo, et al., 2003 ; Redden & Wooten, 2001 ; Reischman & Yarandi, 2002 ).

In relation to specific types of critical thinking and clinical reasoning, it is not possible to draw conclusions because, in both older and recent studies, a tremendous variety of theoretical frameworks were used, making it difficult to combine study results for knowledge development.

One conclusion that can be drawn from the above-cited research, however, is that a variety of thinking abilities are needed for diagnostic accuracy based on the complexity and variety of clinical cases. Abilities in the interpersonal domain are also important for accuracy because the formation of trusting relationships with patients and families enables them to share important personal information ( Carnevali & Thomas, 1993 ; Gordon, 1994 ; Lunney, 2001 ).

In a review of 200 studies related to critical thinking, Tanner ( 2006 ) concluded that one of the factors that influence nurses’ critical thinking was the nurse-patient relationship. In a qualitative study of eight patients’ experiences of how nurses communicate, McCabe ( 2004 ) found that patients experienced differences associated with patient-centered communications versus task-oriented communications.

Regarding patient-centered communications, patients noted that the nurses spoke to them using their names and asking about their experiences. In contrast, patients described task-oriented communications as those that focused on getting a job done, such as administration of medications, without considering the patients’ perspectives.

A lack of patient-centered communications was noted in this study. Patient-centered communications are needed for accurate assessment of human responses. Patient-centered communications are needed for accurate assessment of human responses. Abilities in the technical domain are needed to conduct diagnosis-focused assessments, such as pain or fluid volume shifts, and physical examinations, as well as ability to understand data from equipment, such as mechanical ventilators, all of which affect the quality of data interpretation and thus nursing interventions ( McCaffrey & Ferrell, 1997 ; Puntillo et al., 2003 ; Redden & Wotton, 2001 ; Wilkinson, 2001 ).

The diagnosis of pain is the only nursing diagnosis that has been adequately studied from the perspective of diagnosis-focused assessment, and these studies have shown variations in accuracy (e.g., McCaffrey & Ferrell, 1997 ; Puntillo et al., 2003 ). Nature of the Diagnostic Task. The nature of the diagnostic task refers to clinical situations that influence the interpretation of human responses to health problems and life processes.

Factors within the nature of the diagnostic task that have been studied are relevance of data, amounts of data, and complexity of the diagnostic task ( Lunney, 2001 ). The relevance of data to diagnoses is generally considered as high, moderate, and low relevance ( Carnevali & Thomas, 1993 ; Gordon, 1994 ).

1. Having adequate numbers and types of high relevance data helps to ensure diagnostic accuracy.
2. Studies from the early 1980’s showed that high amounts of low relevance data are associated with low accuracy ( Lunney, 2001 ).
3. An example of this is when nurses collect admission data without a concern for ‘what is the diagnosis.’ The results are data that have no specific implications for helping patients.

In comparing expert and novice cue utilization when making critical care diagnoses, Reischman and Yarandi ( 2002 ) found that experts used more high relevance cues and made more accurate diagnoses. Another factor related to the diagnostic task is the amount of data.

Studies have shown that less information may be associated with higher accuracy ( Lunney, 2001 ). With fewer data points, nurses are more likely to move to diagnostic-specific associations of data with interpretations and arrive at better diagnoses. Regarding the complexity of diagnostic tasks, a few studies have confirmed the logic that increased complexity is associated with lower accuracy ( Lunney, 2001 ).

The more aspects of the human condition that are involved in the diagnostic task, the more complex the situation may be. Consider, for example, a woman who attends a clinic for management of diabetes, presenting with a broken arm, and evidence that the broken arm may have occurred as a result of domestic violence.

This is a much more complex situation to diagnose than just helping such a patient learn how to manage diabetes. If nurses in this clinic seldom encounter such cases, this woman would present a complex case for them. Lack of adequate resources to assist with data collection and interpretation will also increase the complexity of diagnosing such cases.

Situational Contexts, One situational factor that relates to accuracy is the adequacy of available resources. In health care settings, a high nurse-patient ratio is a factor that most likely affects accuracy; however, this factor has not been directly studied to date.

High nurse-patient ratios have been shown to contribute to low quality of care ( Bostick, Rantz, Flesner, & Riggs, 2006 ). With high nurse-patient ratios, nurses do not have time to form trusting relationships with patients, to collect valid and reliable data, or to think about diagnostic decisions. With high nurse-patient ratios, nurses do not have time to form trusting relationships with patients, to collect valid and reliable data, or to think about diagnostic decisions.

Another situational factor that has been studied is the environmental aspects of the setting. The environmental aspect of interruptions of nurses as they are working with patients has been studied through observations of nurses in clinical settings ( Potter, Boxerman, Wolf, Marshall, Grayson, Sledge, & Evanoff, 2004 ; Potter Wolf, Boxerman, Grayson, Sledge, Donagan & Ebanoff, 2005 ; Hedberg & Larsson, 2004 ).

• These researchers reported that interruptions, such as staff inquiries or missing supplies, disturbed the continuity of nurses’ thinking processes while giving care, which most likely affected their decision making.
• The investigators concluded that environmental elements need to be taken into account in studies of nurses’ decision making.

Use of standardized nursing languages such as NANDA International ( NANDA-I, 2007 ) is a third situational factor influencing accuracy of diagnosis. NANDA-I provides the nurse with possible diagnoses to consider and the associated signs and symptoms for making accurate diagnoses.

In a pioneering study conducted in Switzerland, Müller-Staub and colleagues ( 2007 ) demonstrated that teaching nurses about nursing diagnosis and how to accurately diagnose and document diagnoses, interventions, and outcomes had positive effects on the quality of care as measured by documented patient outcomes.

In retrospective analyses of 123, 241 patient admissions in one hospital, Welton and Halloran ( 2005 ) concluded that use of nursing diagnoses was an independent predictor of patient hospital outcomes. When nursing diagnoses were added to the DRG model, the explanatory power of the discharge dataset improved by 30% to 146%, and was significantly associated with patient outcomes, such as length of stay and disposition to nursing homes.

The effects of using standardized nursing languages on nurses’ power to help the children they serve and influence outcomes were measured in a pilot study with 12 nurses and 220 children in two groups ( Lunney, Parker, Fiore, Cavendish, & Pulcini, 2004 ). One group of six nurses used NANDA-I ( 2007 ), the Nursing Interventions Classification (NIC) ( Dochterman & Bulechek, 2004 ), and the Nursing Outcomes Classification ( NOC, Moorhead, Johnson, & Maas, 2004 ).

The other group used only the nursing terms that were included in the computerized software. There were no significant differences between the two groups on nurses’ power and children’s outcomes but, in post-study interviews with the nurses, the nurses reported use of nursing languages helped them to better focus their assessment processes and use these terms as a basis for discussions with children and families.

In a study of the heuristics that nurses used in diagnosing, Ferrario ( 2003 ) concluded that use of standardized nursing terms may make the diagnostic thinking process more efficient. Diagnosis in Nursing is Complex The discipline of nursing, with its focus on the health of human beings, may be the most complex science that exists ( Webster, 1984 ).

The complexity of diagnosing human responses is clearly illustrated in nursing case studies, such as those described in Lunney, 2001. The complexity of the environments in which nurses work was substantiated in both the United States and Sweden. This environmental complexity adds to the challenge of achieving high accuracy ( Bucknall, 2003 ; Hedberg & Larsson, 2004 ; Potter et al., 2004, 2005 ).

Furthermore, lack of attention to the issue of diagnostic accuracy compounds the problem of low accuracy ( Lunney, 1998 ). Accuracy needs to be a stated goal in order to achieve higher accuracy. Implementation of Electronic Health Records The implementation of electronic health records (EHRs) is imminent, with a goal in the United States of having all health records in an electronic format by 2015 ( United States (U.S.) Department of Health & Human Resources, 2004 ).

With implementation of electronic health records (EHRs), the relevance of accurate interpretations of patient data to the quality of nursing care will be greater than it is with paper records ( Institute of Medicine, 2004 ; Olsson, Lymberts, & Whitehouse, 2004 ).

EHRs provide better organization and ease of noting key information such as diagnoses, interventions, and outcomes, so improved continuity of care is expected ( IOM, 2004 ). Nurses’ diagnoses will easily be identified for follow-up by other nurses and for data aggregation to describe and compare patients’ experiences across settings and localities.

The accuracy of nurses’ diagnoses will also influence the effectiveness of interventions provided by health care professionals in other disciplines as they increasingly rely on a nurse’s diagnosis in selecting their specific intervention(s). Accountability for Accuracy Accuracy of nurses’ diagnoses is the foundation for achieving positive outcomes through use of nursing interventions.

Because research studies document variance in nurses’ diagnoses, and variance means that some diagnoses are not accurate, nurses in both practice and education are encouraged to consider their accountability for accuracy of diagnoses. Levin, Lunney, and Krainovich-Miller ( 2005 ), for example, applied the five steps of evidenced-based medicine, as described by Sackett, Strauss, Richardson, Rosenberg, and Haynes ( 2000 ), to show how diagnostic accuracy in nursing can be improved through use of research evidence and patient preferences.

A new PCD model (Population, Cue Cluster, Differential Diagnoses) was proposed for the first step of evidence-based practice, i.e., asking answerable questions. The five-step, evidence-based process, of (a) ask answerable questions, (b) find the best evidence to answer the questions, (c) appraise the validity of the evidence, (d) integrate the evidence with experience and patient preferences, and (e) evaluate the effectiveness of the first four steps, was explained as it pertains to accuracy of nurses’ diagnoses.

Accuracy of nurses’ diagnoses is the foundation for achieving positive outcomes through use of nursing interventions, either with or without the use of standardized nursing diagnoses from NANDA-I or other diagnostic languages. When nurses act on their interpretations of data, they are acting on diagnoses, whether or not the diagnoses are stated.

Accountability for nurses’ accuracy has been limited in the past but may be improving. Prior to 2006, there was very little discussion about this issue in English language journals, whether these journals were primarily research-based, theory-based, or anecdotal.

A 2006 CINAHL search using accuracy of nurses’ diagnoses as the search term, yielded eight articles, including the clinical study of accuracy previously mentioned that was described in two sources, three papers that summarized the research on critical thinking and accuracy, and three position papers, all of which were written by Lunney and others.

A 2006 Medline/PubMed search of the National Library of Medicine database using the same search term yielded 38 studies, but only eight of these were relevant and additional to those in the CINAHL search. These eight studies consisted of a 1980 study and seven studies related to pain.

A 2006 Medline/PubMed search using nurses’ decision making yielded 2000 references, which included many unrelated articles. There were four additional studies of nurses’ data interpretations and 37 studies related to clinical decision making. In these 37 studies, the expectation that nurses interpretations vary was implied.

In April 2007, evidence of increased emphasis on accuracy was noted at a conference of the Association of Common European Nursing Diagnoses Interventions and Outcomes (ACENDIO). The keynote address by Daniel Pesut focused on the importance of clinical reasoning and identifying a keystone diagnosis.

A keystone diagnosis is an alternate term for the most accurate diagnosis; it is the diagnosis that best matches the complexity of the patient’s story and provides guidance for patient outcomes and nursing interventions. Various studies related to accuracy were also presented by researchers. For example, Paquay, Wouters, Debaillie, and Geys, ( 2007 ) studied the convergent and discriminant validity of 101 NANDA nursing diagnoses for 1,952 patients with dependency problems in activities of daily living (ADL).

Nursing diagnoses were formulated for 80% of the patients and most indicators of convergent and discriminant validity of the 101 NANDA diagnoses were highly significant. The authors are planning to study the validity of nurses’ diagnoses in relation to other domains besides ADL and the accuracy of nurses’ diagnoses in relation to diagnostic reasoning.

• Even though wide variations in accuracy can be expected based on complex factors in each of the three categories of the (a) diagnostician; (b) diagnostic task, and (c) situational contexts, as well as the interactions of these three categories, the implications of low accuracy are significant.
• Widespread implementation of EHRs means that data will be aggregated to describe nursing care.

Data that are based on low accuracy diagnoses will be misleading, if not useless. Nurses in practice (staff nurses, leaders, and administrators) and nursing educators need to be more diligent in promoting and measuring the accuracy of nurses’ diagnoses.

Strategies To Promote Accuracy of Nurses’ Diagnoses Nurses both in practice and education can promote accuracy of nurses’ diagnoses by making changes as needed in the three types of factors that influence diagnostic accuracy: the diagnostician, nature of the diagnostic task, and the situational context.

Strategies related to each of these factors are offered below. References, which provide further information on implementing each strategy, are also provided. Diagnostician Nurses and students can be encouraged to recognize ambiguity and find ways to address it,.nurse educators should help students think of themselves as developing diagnosticians.

Strategies for nurses to develop as diagnosticians include assuming the image and role of being a diagnostician, accepting the ambiguity of clinical judgment in nursing, promoting the principle of working in partnership with patients and families, collaborating with interdisciplinary team members, and facilitating the use of critical thinking processes, including reflection.

Brief examples of strategies in each category are provided below. Strategies for nurses to assume the image and role of being a diagnostician can be identified and applied by all nurses. Beginning with the very first nursing course, nurse educators should help students think of themselves as developing diagnosticians.

1. This image needs to be reinforced and supported in all nursing education experiences, whether they be in institutions of higher education or nursing practice settings, through the teaching methods and content selected.
2. Teaching methods that require students to think, make decisions, and develop clinical judgments are needed.

Teaching through lectures provides knowledge but does not help students think through how to use the knowledge in clinical settings. In contrast, problem-based learning, a technique that requires students to find the answers to problems, helps students develop problem-solving skills, for example, by asking students to identify the diagnosis ( Amos & White, 1998 ; Beers, 2005 ; Williams, 2001 ).

Although some faculty fear that adequate content will not be presented when using problem-based learning, Beers ( 2005 ) reported that there were no significant differences in knowledge acquisition, as indicated by objective test scores, between two groups of students who received the same content, but with one group receiving a lecture and the other receiving problem-based learning.

Additionally Williams ( 2001 ) noted that problem-based learning, active learning, and self-directed learning provided additional learning advantages over lecture methods in clinical settings and in application of previous knowledge to actual clinical cases.

One advantage is that it gives students opportunities to use the types of thinking that they later will need for clinical application of knowledge. Willingham ( 2007 ) a cognitive scientist, says that teachers should give students repeated opportunities to practice the types of thinking that will be needed in real situations.

Another advantage is that students learn to answer questions posed by the teacher as well as to ask questions that can be answered through the use of research and other types of evidence. Since problem-based learning is not always feasible without changing an entire curricula and may be costly ( Beers, 2005 ), other strategies for active, reflective, and integrated learning should be considered.

• For example, in practice settings, staff nurses can assume the role of diagnostician and present the rationale for the diagnosis they have selected to be critiqued by their peers.
• Administrators can encourage nurses’ discussions of intellectual, interpersonal, and technical aspects of nursing, rather than encouraging only task completion.

In reference to accepting the ambiguities of clinical judgments in nursing, nursing students need to be told, and nurses need to be reminded, that because they are helping people with complex responses to health problems and life processes, errors in diagnosing are possible and must be acknowledged.

Nurses and students can be encouraged to recognize ambiguity and find ways to address it, for example, by seeking consultation from other health care providers, validating impressions with patients and families, and becoming acquainted with current, professional literature. Lunney ( 2001 ) and Sandstrom ( 2006 ) used small group discussions of written case studies in which the group members discussed different possible diagnoses to help students develop tolerance for ambiguity as they noted that other learners, too, had difficulty identifying the best diagnosis.

Use of case studies can enable learners to comprehend the complexity of diagnosing human responses as they learn to analyze, synthesize, evaluate, and apply knowledge. Practicing nurses can support tolerance for ambiguity by accepting the difficulties that new nurses’ experience with data interpretations and helping them to identify variations that occur with given contextual factors such as age and culture.

• Nurse administrators can support nurses in dealing with ambiguity by marketing the value of using nursing diagnoses, along with the complexities of doing so, as they interact with other disciplines, other administrators, and the public.
• Patients are more likely to work toward the achievement of improved health outcomes if they agree with the nurses’ diagnoses and proposed interventions.

Educators and practicing nurses can apply the principle of working in partnership with patients and families and the principle of working in collaboration with interdisciplinary team members, as standards of care. Partnership processes with patients and families are essential for diagnosing human responses.

1. Patients are more likely to work toward the achievement of improved health outcomes if they agree with the nurses’ diagnoses and proposed interventions.
2. Leenerts and Teel ( 2006 ) developed a promising strategy to achieve partnership, called Self Care Talk.
3. This relational conversation method is a combination of the four specific communication skills of (a) listening with intent (e.g., listening for habits, concerns, and needs related to self care), affirming emotions (e.g., conveying recognition and respect for emotions), creating relational images (e.g., helping patients to create positive images of past experiences that they can relate to current experiences), and using planned enactment (e.g., asking patients to describe plans for self care).

Collaboration with interdisciplinary team members is essential because other health providers may have knowledge and insights about patients and families that are not known by nurses. The need for critical thinking skills has been explained by many nurse theorists and researchers as previously cited.

Nowledge of tools for critical thinking can facilitate the making of clinical judgments. Journal writing is a tool that can be used by educators to help students learn to reflect on their critical thinking in clinical decision making ( Lunney, 2001 ). For experienced nurses, the development and use of appropriate heuristics can help them draw upon their past experiences to strengthen their clinical judgments.

The representativeness heuristic, described by Brannon and Carson ( 2003 ) and Thompson ( 2003 ) is one such heuristic. The representativeness heuristic is a characteristic of experienced clinicians in which “detailed analyses and probability assessments are replaced with representations that include computations of similarity to previous experiences, evaluation of associations and exemplars, and attributions of causality” ( Ferrario, 2003, p.44 ).

This heuristic increases the efficiency and effectiveness of diagnostic reasoning. Some heuristics, however, such as overconfidence have negative effects on clinical judgment. Thompson ( 2003 ) provided strategies to combat overconfidence and other types of biases that develop with experience. Developing the ability to find and use appropriate research for a given care situation is one such strategy.

Another strategy is helping nurses to “know what they do not know and revise estimates of correctness accordingly” (p.233), which is known as calibration. Two ways to increase calibration is to think of reasons that a decision might be wrong and to identify other possible explanations besides the current explanation being considered.

• When overconfidence is addressed by others, it needs to be discussed with respect and care so nurses can maintain their self esteem while improving their accuracy as diagnosticians.
• The questioning of students and nurses in relation to the care of patients for purposes of stimulating critical thinking is a method that can easily be incorporated into lecture, clinical supervision, and preceptorships ( Browne & Keeley, 2007 ; Rubenfeld & Scheffer, 2006 ).

The use of higher level questions, i.e. questions which require learners to analyze, synthesize, evaluate, or apply knowledge, as compared with questions that ask only for a recall of knowledge, also promotes critical thinking ( Phillips & Duke, 2001 ).

• Lecturers, for example, can use these higher level questions throughout a class to stimulate students’ thinking about the related issues and responding to the teacher’s questions, rather than merely writing what the teacher has said.
• Use of higher level questions can be used throughout the curriculum to develop the critical thinking skills of the students.

Nature of the Diagnostic Task One strategy to manage the complexity of clinical situations in nursing is identification of diagnoses, interventions, and outcomes that are common with specific populations, such as diabetic patients, and development of standards of practice based on this information that will provide decisional support for nurses.

• A consensus validation research process can be used, for example, to identify the common diagnoses, interventions, and outcomes for local (geographical) populations.
• Carlson, 2006 ) reported that this strategy helped nurse stakeholders reduce the number of NANDA-I ( 2007 ) nursing diagnoses to consider from 197 to about 20 that were relevant to a given population and local setting.

Additional decision-support tools continue to be developed to assist nurses with clinical decision making. Some of the current tools are concept mapping ( Ferrario, 2003 ; 2004a ; 2004b ), N-CODES ( O’Neill, Dluhy, & Chin, 2005 ; O’Neill, Dluhy, Hansen, & Ryan, 2006 ), decision analysis ( Narayan, Corcoran-Perry, Drew, Hoyman, & Lewis, 2003 ), and software known as CHOICE ( Ruland, & Bakken, 2003 ).

Educators and administrators should explore the possibilities of using one or more of these decision-support tools as they work to strengthen nurses’ diagnostic skills Situational Context In health care settings, there are many situational practices that can be modified to increase accuracy of diagnosis.

Some of these practices include appropriate selection of software for EHRs, development of environments that support nurse-patient partnerships, provision of feedback to nurses about the patient outcomes based on their diagnoses, adoption of policies that support the goal of accuracy, changing of admission protocols, marketing to the public nurses’ diagnoses of human responses, and funding of resources to address the common diagnoses, interventions, and outcomes for the populations they serve.

1. The following paragraphs will provide hints and resources to assist in utilizing these practices.
2. Software structures and processes need to be examined for their ability to support the nursing process.
3. The structure, for example, should include separate screens for assessment data and the diagnostic process so that the adequacy of data support for diagnoses can be retrospectively examined.

There must be sufficient space in the sections for assessment data and diagnoses so that research-based diagnoses and their data support can be added or changed in accordance with research findings. Also important is the availability of electronic libraries to provide evidence for the effectiveness of nursing interventions.

1. The nursing concepts in NANDA-I, NIC and NOC have had extensive research support.
2. Processes that need to be examined are the linkages of assessment data, diagnoses, interventions, and nursing outcomes.
3. In relation to nurse-patient partnerships, nurses can be the leaders in health care settings for implementation of partnership models of patient care.

For example, health care agencies can encourage nurses to set up contracts with patients and encourage patient and family signatures as an indication of partnership (similar to informed consent) in addressing the diagnoses for which they agree. Patient and family agreement with a nurse’s diagnosis can help the patient and family understand themselves and increase their willingness to work with health care personnel to improve their health status.

1. Provision of feedback to nurses about the patient outcomes based on their diagnoses, especially in acute care settings, will help nurses to experience the satisfaction of making accurate diagnoses and providing the appropriate associated interventions.
2. Use of the NOC with its five-point scales will help nurses to see positive outcomes that occur before patients leave acute care units ( Moorhead et al., 2004 ).

It would also be helpful to create organizational feedback systems so nurses could evaluate the appropriateness of their diagnoses and interventions based on the patient’s long term outcomes.health care systems should be teaching and supporting nurses to rule in and rule out specific competing diagnoses.

Adoptions of policies that support the goal of diagnostic accuracy are important because agency policies provide organizational frameworks for nursing practice. If policies and procedures suggest that nurses should focus on tasks, such as distributing medications, with little support for thinking about patient data for purposes of accurate diagnoses, one can expect the rates of accuracy will be lower.

The evidence presented in this article indicates that health care systems should be teaching and supporting nurses to rule in and rule out specific competing diagnoses instead of collecting patient data just to complete assessment forms. Admission protocols should be changed if they suggest that nurses must document a specific number of nursing diagnoses at the end of the initial assessment.

First, accurate diagnoses are made when sufficient data is accumulated to support the diagnoses, not necessarily at the end of an admission assessment. It may not be possible to identify the most appropriate problems to address upon admission. Second, initially there may be no nursing care problems or nursing diagnoses in some patients.

This is illustrated by a clinical study of nurses’ accuracy in diagnosing the psychosocial problems of 160 patients in three hospitals in which a staff nurse and two clinical nurse specialist assessed the patients and found that 28 (17.5%) of the patients had no psychosocial problems that needed to be addressed by nurses ( Lunney, et al., 1997 ).

1. Nurse administrators should also consider marketing the use of nurses’ diagnoses of human responses to the public.
2. This will help the people who use the health care agency know what to expect from the nurses and work more effectively with the nurses to identify the responses to health problems and life processes for which they desire nursing assistance.

It will also reinforce the behaviors of nurses who focus more diligently on making accurate diagnoses of patients’ responses. Finally, adequate agency funding of resources is necessary for nurses to accurately diagnose and treat to their patients. Needed resources include reference materials related to human response diagnoses, such as the latest books by NANDA-I ( 2007 ), research reports to guide evidenced-based practice ( Levin, et al., 2005 ), opportunities for continuing education, the time and support to allow nurses to think critically and use clinical reasoning skills, and the availability of other providers to help identify the most appropriate diagnoses.

1. Continuing education may be needed to bring nurses up-to-speed and keep them up-to-date in these areas.
2. Time for nurses to think and to collaborate with patients, families, and other health providers is an important resource for improving diagnostic accuracy among nurses.
3. This appeal for nurses in practice and education to address the accuracy of nursing diagnoses is clearly supported by the research evidence, the complexity of diagnosis in nursing, and the impending implementation of EHRs.

The accountability of nurses to address accuracy has been weak in the past but may be improving. Strategies to further demonstrate accountability by improving accuracy have been provided in this article. Margaret Lunney, RN, PhD E-mail: [email protected] Margaret Lunney is a Professor and Graduate Nursing Programs Coordinator at the College of Staten Island, the City University of the New York (CUNY), and Doctoral Faculty at CUNY’s Graduate Center.

1. For 25 years, her research and professional activities have focused on community health nursing, critical thinking, and the concept of accuracy of nurses’ diagnoses, measurement of accuracy, and use of standardized nursing languages.
2. Among the honors she received are the 2001 Distinguished Nurse Researcher Award from the New York State Nurses’ Foundation and a 2007 Fulbright Award as research consultant and lecturer in Japan.

She received her M.S. in nursing from Hunter College, CUNY, and her PhD in Nursing Science from New York University. © 2008 OJIN: The Online Journal of Issues in Nursing Article published January 31, 2008