Reviewing the Literature and Theory. Health, Medicine, Nursing Essay

Reviewing the Literature and Theory: Evidence Table
Name
Institutional Affiliation
Evidence Table
Source#1: Abimanyi-Ochom, J. et al. (2019). Strategies to reduce diagnostic errors: a systematic review. BMC Medical Informatics & Decision Making 19, 174, 1-12, https://doi.org/10.1186/s12911-019-0901-1

Description of issue

Statistical Information

Rates of Incidence/Morbidity/Mortality

Support Proposed Change?

Level of Evidence

The article describes the issue of patient misdiagnosis that while several studies on strategies to reduce diagnostic errors have been conducted, there is little evidence to identify specific and practice approach used in clinical settings and there are biases in studies, especially from the United States (Abimanyi-Ochom et al., 2019)

Yes, there is a bias in 73% of the 19 studies from the United States that have limited possible interventions to prevent patient misdiagnosis (Abimanyi-Ochom et al., 2019).

None

Yes, the article supports trigger algorithms such as alert and computer-based systems to reduce delayed and accuracy of diagnosis.

Grade B/Level II evidence (Practice Recommendation) (Burns, Rohrich, & Chung, 2011).

Source#2: Graber, M.L. (2013). The incidence of diagnostic error in medicine. BMJ Quality & Safety, 22, ii21-ii27, http://dx.doi.org/10.1136/bmjqs-2012-001615

Description of issue

Statistical Information

Rates of Incidence/Morbidity/Mortality

Support Proposed Change?

Level of Evidence

In the article, Graber (2013) describes misdiagnosis issues result in patient harm, and deaths and, while different approaches such as diagnostic testing audits have been attempted, none is effective in practice.

Misdiagnosis occurs at the rate of 10-15% in the United States (Graber, 2013).

Incidence rates of 10-15% in the United States. 73% of misdiagnoses result in serious patient harm (Graber, 2013).

Yes, the article supports trigger tools such as the “Global Trigger Tool” to capture rates of misdiagnosis from patient electronic records (Grabber, 2013)

Grade A/Level I evidence (Strong recommendation of practice) (Burns, Rohrich, & Chung, 2011).

Source#3: Hogan, W.R., & Ceusters, W. (2016). Diagnosis, misdiagnosis, lucky guess, hearsay, and more: an ontological analysis. Journal of Biomedical Semantics, 7, 54, https://dx.doi.org/10.1186%2Fs13326-016-0098-5

Description of issue

Statistical Information

Rates of Incidence/Morbidity/Mortality

Support Proposed Change?

Level of Evidence

Hogan and Ceusters (2016) recognize the significance of patient diagnosis data in quality care. However, the leading concern in improving quality is errors and provenance in this data, especially in cases involving lucky guesses, and justified layperson’s beliefs that a disease is present.

None

None

Yes, Hogan and Ceusters (2016) recommend the use of data capture approaches in electronic health records (EHR), including other computerized systems to facilitate the capture of diagnostic assertions will minimal bias.

Grade A/Level I evidence (Strong recommendation) (Hogan & Ceusters, 2016).

Source#4: Kim, M.O., Coiera, E., & Magrabi, F. (2017). Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. Journal of the American Medical Informatics Association, 24(2), 246-250, https://doi.org/10.1093/jamia/ocw154

Description of issue

Statistical Information

Rates of Incidence/Morbidity/Mortality

Support Proposed Change?

Level of Evidence

While the adoption of computerized systems has been shown to reduce patient diagnosis errors, Kim, Coiera, and Magrabi (2017) identify some of the errors with IT systems that can potentially impact patient care.

Yes, statistical information on errors caused by IT systems such as delays in diagnosis and wrong patient diagnosis (Kim, Coiera, & Magrabi, 2017).

Incidences of 14.41% of problems in 6 countries including the United States. 29% of issues caused by computerized provider order entry (CPOE) and 29% by all forms of IT systems.

Optional since the authors find several problems with computerized systems in patient care especially patient diagnosis (Kim, Coiera, & Magrabi, 2017).

Grade D/Level V evidence meaning clinicians should attempt other options available (Hogan & Ceusters, 2016).

Source#5: McDonald, K.M., Bryce, C.L., & Graber, M.L. (2013). The patient is in: patient involvement strategies for diagnostic error mitigation. BMJ Quality & Safety, 13 (22), ii33-ii39, http://dx.doi.org/10.1136/bmjqs-2012-001623

Description of issue

Statistical Information

Rates of Incidence/Morbidity/Mortality

Support Proposed Change?

Level of Evidence

It is only recently that the issue of patient misdiagnosis has gained importance although many patients have suffered from this problem for a long time (McDonald, Bryce, & Graber, 2013).

3/5 Americans or 63% are concerned about errors in patient diagnosis. 5.3 of issues involve missed appointments for tests, breakdown in the diagnostic process, and delays in diagnosis. 44% of patients missed tests among the 587 patients with lung cancer.

None

No, the authors recommend other strategies such as patient involvement in the care process to reduce the cases of misdiagnosis (McDonald, Bryce, & Graber, 2013).

Grade C/Level II, III, or IV. Inconsistent findings and instead patients need to be involved (Hogan & Ceusters, 2016).

Source#6: Neale, G., Hogan, H., & Sevdalis, N. (2011). Misdiagnosis: an analysis based on case record review with proposals aimed to improve diagnostic processes. Clinical Medicine, 11(4), 317-321, https://dx.doi.org/10.7861%2Fclinmedicine.11-4-317

Description of issue

Statistical Information

Rates of Incidence/Morbidity/Mortality

Support Proposed Change?

Level of Evidence

Neale, Hogan, and Sevdalis (2011) state that errors in patient diagnosis comprises 10% of all adverse events in clinical practice. However, limited studies have investigated ways to improve diagnostic measures (Neale, Hogan, & Sevdalis, 2011).

Early studies conducted by Harvard showed 14% of all errors in hospital settings are diagnostic errors. This exceeds medication errors that stood at 9%. In the U.S., 26% of medical-legal claims are due to patient misdiagnosis, a percentage higher than surgical accidents which are 25% (Neale, Hogan, & Sevdalis, 2011)

96% of total incidents of misdiagnosis are caused by cognitive errors while 65% are system-based failures.

No, analytical reasoning is recommended instead (Neale, Hogan, & Sevdalis, 2011)

Grade C/Level II, III, or IV. Other approaches recommended (Hogan & Ceusters, 2016).

Source#7: Nurek, M., Kostopoulo, O., Delaney, B.C., & Esmail, A. (2015). Reducing diagnostic errors in primary care. A systematic meta-review of computerized diagnostic decision support systems by the LINNAEUS collaboration on patient safety in primary care. European Journal of General Practice, 21 (sup 1), 8-13, https://doi.org/10.3109/13814788.2015.1043123

Description of issue

Statistical Information

Rates of Incidence/Morbidity/Mortality

Support Proposed Change?

Level of Evidence

The authors, Nurek, Kostopoulo, Delaney and Esmail (2015) recommend the use of computerized diagnostic decision support systems (CDSS) in performing cognitive tasks during diagnosis. These diagnostic tasks, if not properly handled, can result in a significant number of adverse events in healthcare settings (Nurek, Kostopoulou, Delaney & Esmail, 2015).

Yes, most cases of diagnostic errors are due to cognitive factors.

Cognitive factors result in 55% of all diagnostic errors in the United States

Yes, the authors recommend the use of CDSS though some implementations are proposed to improve the system.

Grade B/ Level II, III, or IV (Hogan & Ceusters, 2016).

Source#8: Olson, A., et al. (2019). Competencies for improving diagnosis: an interpersonal framework for education and training in health care. Diagnosis, 6(4), 335-341, h...