Potential Unintended Consequences Due to Medicare’s “No Pay for Errors Rule”? A Randomized Controlled Trial of an Educational Intervention with Internal Medicine Residents

Medicare has selected 10 hospital-acquired conditions for which it will not reimburse hospitals unless the condition was documented as “present on admission.” This “no pay for errors” rule may have a profound effect on the clinical practice of physicians. To determine how physicians might change their behavior after learning about the Medicare rule. We conducted a randomized trial of a brief educational intervention embedded in an online survey, using clinical vignettes to estimate behavioral changes. At a university-based internal medicine residency program, 168 internal medicine residents were eligible to participate. Residents were randomized to receive a one-page description of Medicare’s “no pay for errors” rule with pre-vignette reminders (intervention group) or no information (control group). Residents responded to five clinical vignettes in which “no pay for errors” conditions might be present on admission. Primary outcome was selection of the single most clinically appropriate option from three clinical practice choices presented for each clinical vignette. Survey administered from December 2008 to March 2009. There were 119 responses (71%). In four of five vignettes, the intervention group was less likely to select the most clinically appropriate response. This was statistically significant in two of the cases. Most residents were aware of the rule but not its impact and specifics. Residents acknowledged responsibility to know Medicare documentation rules but felt poorly trained to do so. Residents educated about the Medicare’s “no pay for errors” were less likely to select the most clinically appropriate responses to clinical vignettes. Such choices, if implemented in practice, have the potential for causing patient harm through unnecessary tests, procedures, and other interventions

Using Clinical Decision Support to Maintain Medication and Problem Lists: A Pilot Study to Yield Higher Patient Safety

To Investigate Whether Clinical Decision Support that Automates the Matching of Ordered Drugs to Problems (Clinical Diagnoses) on the Problem List Can Enhance the Maintenance of Both Medication and Problem Lists in the Electronic Medical Record, We Designed a Clinical Decision Support System to Match Ordered Drugs on the Medication List and Ongoing Problems on the Problem List. We Evaluated the Capability and Performance of This Clinical Decision Support System in Medication-Problem Matching using Physician Expert Chart Audits to Match Ordered Drugs to Ongoing Clinical Problems. a Clinical Decision Support System Was Shown to Be Useful in Improving Medication-Problem Matches in 140 Randomly Selected Audited Patient Encounters in Three Inpatient Units. Enhanced Maintenance of Both the Medication and Problem Lists Can Permit the Exploitation of Advanced Decision Support Strategies that Yield Higher Patient Safety. © 2008 IEEE

Comparison of a prototype for indications-based prescribing with 2 commercial prescribing systems

Importance: The indication (reason for use) for a medication is rarely included on prescriptions despite repeated recommendations to do so. One barrier has been the way existing electronic prescribing systems have been designed. Objective: To evaluate, in comparison with the prescribing modules of 2 leading electronic health record prescribing systems, the efficiency, error rate, and satisfaction with a new computerized provider order entry prototype for the outpatient setting that allows clinicians to initiate prescribing using the indication. Design, Setting, and Participants: This quality improvement study used usability tests requiring internal medicine physicians, residents, and physician assistants to enter prescriptions electronically, including indication, for 8 clinical scenarios. The tool order assignments were randomized and prescribers were asked to use the prototype for 4 of the scenarios and their usual system for the other 4. Time on task, number of clicks, and order details were captured. User satisfaction was measured using posttask ratings and a validated system usability scale. The study participants practiced in 2 health systems\u27 outpatient practices. Usability tests were conducted between April and October of 2017. Main Outcomes and Measures: Usability (efficiency, error rate, and satisfaction) of indications-based computerized provider order entry prototype vs the electronic prescribing interface of 2 electronic health record vendors. Results: Thirty-two participants (17 attending physicians, 13 residents, and 2 physician assistants) used the prototype to complete 256 usability test scenarios. The mean (SD) time on task was 1.78 (1.17) minutes. For the 20 participants who used vendor 1\u27s system, it took a mean (SD) of 3.37 (1.90) minutes to complete a prescription, and for the 12 participants using vendor 2\u27s system, it took a mean (SD) of 2.93 (1.52) minutes. Across all scenarios, when comparing number of clicks, for those participants using the prototype and vendor 1, there was a statistically significant difference from the mean (SD) number of clicks needed (18.39 [12.62] vs 46.50 [27.29]; difference, 28.11; 95% CI, 21.47-34.75; P \u3c .001). For those using the prototype and vendor 2, there was also a statistically significant difference in number of clicks (20.10 [11.52] vs 38.25 [19.77]; difference, 18.14; 95% CI, 11.59-24.70; P \u3c .001). A blinded review of the order details revealed medication errors (eg, drug-allergy interactions) in 38 of 128 prescribing sessions using a vendor system vs 7 of 128 with the prototype. Conclusions and Relevance: Reengineering prescribing to start with the drug indication allowed indications to be captured in an easy and useful way, which may be associated with saved time and effort, reduced medication errors, and increased clinician satisfaction

Two Algorithms for the Reorganisation of the Problem List by Organ System

Objective Long Problem Lists Can Be Challenging to Use. Reorganization of the Problem List by Organ System is a Strategy for Making Long Problem Lists More Manageable. Methods in a Small-Town Primary Care Setting, We Examined 4950 Unique Problem Lists over 5 Years (24 033 Total Problems and 2170 Unique Problems) from Our Electronic Health Record. All Problems Were Mapped to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) and SNOMED CT Codes. We Developed Two Different Algorithms for Reorganizing the Problem List by Organ System based on Either the ICD-10-CM or the SNOMED CT Code. Results the Mean Problem List Length Was 4.9±4.6 Problems. the Two Reorganization Algorithms Allocated Problems to One of 15 Different Categories (12 Aligning with Organ Systems). 26.2% of Problems Were Assigned to a More General Category of  € Signs and Symptoms\u27 that Did Not Correspond to a Single Organ System. the Two Algorithms Were Concordant in Allocation by Organ System for 90% of the Unique Problems. Since ICD-10-CM is a Monohierarchic Classification System, Problems Coded by ICD-10-CM Were Assigned to a Single Category. Since SNOMED CT is a Polyhierarchical Ontology, 19.4% of Problems Coded by SNOMED CT Were Assigned to Multiple Categories. Conclusion Reorganization of the Problem List by Organ System is Feasible using Algorithms based on Either ICD-10-CM or SNOMED CT Codes, and the Two Algorithms Are Highly Concordant

Data gaps in electronic health record (EHR) systems: An audit of problem list completeness during the COVID-19 pandemic

OBJECTIVE: To evaluate the completeness of diagnosis recording in problem lists in a hospital electronic health record (EHR) system during the COVID-19 pandemic. DESIGN: Retrospective chart review with manual review of free text electronic case notes. SETTING: Major teaching hospital trust in London, one year after the launch of a comprehensive EHR system (Epic), during the first peak of the COVID-19 pandemic in the UK. PARTICIPANTS: 516 patients with suspected or confirmed COVID-19. MAIN OUTCOME MEASURES: Percentage of diagnoses already included in the structured problem list. RESULTS: Prior to review, these patients had a combined total of 2841 diagnoses recorded in their EHR problem lists. 1722 additional diagnoses were identified, increasing the mean number of recorded problems per patient from 5.51 to 8.84. The overall percentage of diagnoses originally included in the problem list was 62.3% (2841 / 4563, 95% confidence interval 60.8%, 63.7%). CONCLUSIONS: Diagnoses and other clinical information stored in a structured way in electronic health records is extremely useful for supporting clinical decisions, improving patient care and enabling better research. However, recording of medical diagnoses on the structured problem list for inpatients is incomplete, with almost 40% of important diagnoses mentioned only in the free text notes

How Does Computerized Provider Order Entry Implementation Impact Clinical Care Quality, Cycle Time, and Physician Job Demand Over Time?

Hospitals are complex organizations where inefficiencies and medical errors are unfortunately all too common. In order to increase both the efficiency and quality of care delivery, hospitals have turned to healthcare information technology (HIT) in general, and computerized provider order entry (CPOE) in particular. CPOE can impact clinical care process in a number of different ways, however, two things that it brings are process standardization and improvements in documentation quality. Our study traces physicians\u27 response to a CPOE implementation at a large urban Southeastern hospital. Our results reveal an interesting progression over time in how process standardization and documentation quality impact physician perceptions of job demands and process benefits. The progression surfaces the mechanisms through which physicians develop benefit perceptions for turnaround time and medical error reduction quality and how they experience job demands during a CPOE implementation

Impact of Electronic Health Records On Patient Outcomes

With the passing of the HITECH Act, EHRs have come into prominence and sharper focus, due to efforts by the government to push for a national adoption of EHRs into our healthcare system. This push for a national adoption of EHRs is based on the premise that it will help improve the quality delivery of health care services and reduce costs. However, this push for a “national adoption” has experienced mixed results. This study was designed to assess the impact of EHRs post-2009, the year of the HITECH Act, to review some of the key contributing factors to this mixed results. The findings of the study show that implementing EHRs has positively impacted the US health care system in a variety of ways. Thus, while we may conclude that the implementation EHRs have definitely improve the quality of healthcare in the US, and show great promise for the future of health care in this nation, there is still much work to be done to ensure that their full potential is realized. These findings will contribute towards the ongoing effort to expand the implementation of HER by making them secure and easy to use

Computerized Physician Order Entry of Medications and Clinical Decision Support Can Improve Problem List Documentation Compliance

The Problem List is a Key and Required [1] Element of Both the Paper Record and EMR. Weed [2] and [3] Popularized the Use of the Problem List in an Influential Book and Papers. Benson Et Al [4] Have Argued that the Problem List and the Medication Record Are Particularly Useful for Providing an overview of Patients\u27 Significant Diagnoses and Treatments. If Wellstructured, Reliable, and Consistent, They Can Also Contribute Substantially to the Quality of Patient Care. an Accurate Problem List Facilitates Automated Decision Support, Clinical Research, Data Mining, and Patient Care [4], [5] and [6]. the Joint Commission on the Accreditation of Healthcare Organizations [1] Mandates Maintenance of a Problem List. the Problem List Can Be a Useful Tool in Both Paper Records and EMRs for Organizing Physician Notes, Making Patient Rounds, and Signing Out Patients to Covering Physicians [7] and [8]