The Patient Safety Adoption Framework: A Practical Framework to Bridge the Know-Do Gap.

OBJECTIVES: Many patient safety initiatives fail to be adopted and implemented, even when proven effective. This creates the well-recognized know-do gap, referring to the discrepancy between what healthcare workers know should be done based on evidence and what takes place in practice. We aimed to develop a framework to improve the adoption and implementation of patient safety initiatives. METHODS: We conducted a background literature review followed by qualitative interviews with patient safety leaders to identify barriers and facilitators to adoption and implementation. Inductive thematic analysis was used to generate themes that informed the development of the framework. We used a consensus-building approach to co-create the framework and guidance tool with an Ad Hoc Committee made up of subject-matter experts and patient family advisors. The framework was tested for utility, feasibility, and acceptability through qualitative interviews. RESULTS: The Patient Safety Adoption Framework contains 5 domains and 6 subdomains. The domains are leadership (subdomains: prioritization, accountability, governance), culture and context, process (subdomains: co-creation, high reliability, engagement), meaningful measurement, and person-centeredness. A guidance tool was developed to provide practical guidance for improvement teams using the framework. Testing affirmed the framework and guidance tool with a high degree of acceptability, feasibility, and utility among implementers and subject-matter experts. CONCLUSIONS: The Patient Safety Adoption Framework provides the essential components necessary to facilitate the adoption and implementation of patient safety initiatives. The framework offers a roadmap for healthcare organizations striving to close the know-do gap.

A Pilot Study of the Serious Illness Conversation Guide in a Dialysis Clinic.

Clinician-led conversations about future care priorities occur infrequently with end-stage renal disease (ESRD) patients on dialysis. This was a pilot study of structured serious illness conversations using the Serious Illness Conversation Guide (SICG) in a single dialysis clinic to assess acceptability of the approach and explore conversation themes and potential outcomes among patients with ESRD. Twelve individuals with ESRD on dialysis from a single outpatient dialysis clinic participated in this study. Participants completed a baseline demographics survey, engaged in a clinician-led structured serious illness conversation, and completed an acceptability questionnaire. Conversations were recorded, transcribed and thematically analyzed. The average age of participants was 68.8 years. The conversations averaged 20:53 in length. Ten participants (83%) felt that the conversation was held at the right time in their clinical course and eleven participants (91%) felt that it was worthwhile. Most participants (73%) reported neutral feelings about clinician use of a printed guide. Eleven participants (91%) reported no change in anxiety about their illness following the conversation, and five participants (42%) reported that the conversation increased their hopefulness about future quality of life. Thematic analysis revealed common perspectives on dialysis including that participants view in-center hemodialysis as temporary, compartmentalize their kidney disease, perceive narrowed life experiences and opportunities, and believe dialysis is their only option. This pilot study suggests that clinician-led structured serious illness conversations may be acceptable to patients with ESRD on dialysis. The themes identified can inform future serious illness conversations with dialysis patients.

Development, Implementation, and Outcomes of a Serious Illness Care Community of Practice.

CONTEXT: Communities of Practice (CoP) can help geographically separated individuals who share a joint enterprise, mutual engagement, and a repertoire of tools to gain, maintain, and implement new skills, including serious illness communication. OBJECTIVES: To investigate the health system uptake, implementation and outcomes of the Serious Illness Community of Practice (SICoP). METHODS: Participants included members of the online SICoP, including participants from all 50 states in the United States and 44 countries, interested in implementation of the Serious Illness Care Program. Yearly surveys asked members about their program's composition, completed trainings, number of serious illness conversations, and utilization of the online SICoP tools and resources. RESULTS: Over four years, membership in the SICoP increased from 429 to 1,912, with an estimated 17,785 clinicians trained and 38,945 serious illness conversations conducted. Members have continued to utilize and modify the SICoP resources. CONCLUSIONS: Utilizing a CoP has contributed to improving the health care system implementation and process outcomes of serious illness communication training. KEY MESSAGE: This article describes the implementation and health system outcomes of a Community of Practice developed to support serious illness communication. The results indicate that the membership of the community grew and that the community supported growth in the number of trained clinicians and the number of serious illness conversations conducted.

A systematic intervention to improve serious illness communication in primary care: Effect on expenses at the end of life.

BACKGROUND: At a population level, conversations between clinicians and seriously ill patients exploring patients' goals and values can drive high-value healthcare, improving patient outcomes and reducing spending. METHODS: We examined the impact of a quality improvement intervention to drive better communication on total medical expenses in a high-risk care management program. We present our analysis of secondary expense outcomes from a prospective implementation trial of the Serious Illness Care Program, which includes clinician training, coaching, tools, and system interventions. We included patients who died between January 2014 and September 2016 who were selected for serious illness conversations, using the "Surprise Question," as part of implementation of the program in fourteen primary care clinics. RESULTS: We evaluated 124 patients and observed no differences in total medical expenses between intervention and comparison clinic patients. When comparing patients in intervention clinics who did and did not have conversations, we observed lower average monthly expenses over the last 6 ($6297 vs. $8,876, p = 0.0363) and 3 months ($7263 vs. $11,406, p = 0.0237) of life for patients who had conversations. CONCLUSIONS: Possible savings observed in this study are similar in magnitude to previous studies in advance care planning and specialty palliative care but occur earlier in the disease course and in the context of documented conversations and a comprehensive, interprofessional case management program. IMPLICATIONS: Programs designed to drive more, earlier, and better serious illness communication hold the potential to reduce costs. LEVEL OF EVIDENCE: Prospectively designed trial, non-randomized sample, analysis of secondary outcomes.

Development and evaluation of a novel user interface for reviewing clinical microbiology results.

Background: Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review. Methods: We observed and informally interviewed clinicians to determine areas in which existing microbiology review tools were lacking. We developed a new tool that reorganizes microbiology results by time and organism. We conducted a scenario-based usability evaluation to compare the new tool to existing legacy tools, using a balanced block design. Results: The average time-on-task decreased from 45.3 min for the legacy tools to 27.1 min for the new tool (P < .0001). Total errors decreased from 41 with the legacy tools to 19 with the new tool (P = .0068). The average Single Ease Question score was 5.65 (out of 7) for the new tool, compared to 3.78 for the legacy tools (P < .0001). The new tool scored 88 ("Excellent") on the System Usability Scale. Conclusions: The new tool substantially improved efficiency, accuracy, and usability. It was subsequently integrated into the electronic health record and rolled out system-wide. This project provides an example of how clinical and informatics teams can innovative alongside a commercial Electronic Health Record (EHR).

A Picture is Worth 1,000 Words. The Use of Clinical Images in Electronic Medical Records.

OBJECTIVE: To understand how clinicians utilize image uploading tools in a home grown electronic health records (EHR) system. METHODS: A content analysis of patient notes containing non-radiological images from the EHR was conducted. Images from 4,000 random notes from July 1, 2009 - June 30, 2010 were reviewed and manually coded. Codes were assigned to four properties of the image: (1) image type, (2) role of image uploader (e.g. MD, NP, PA, RN), (3) practice type (e.g. internal medicine, dermatology, ophthalmology), and (4) image subject. RESULTS: 3,815 images from image-containing notes stored in the EHR were reviewed and manually coded. Of those images, 32.8% were clinical and 66.2% were non-clinical. The most common types of the clinical images were photographs (38.0%), diagrams (19.1%), and scanned documents (14.4%). MDs uploaded 67.9% of clinical images, followed by RNs with 10.2%, and genetic counselors with 6.8%. Dermatology (34.9%), ophthalmology (16.1%), and general surgery (10.8%) uploaded the most clinical images. The content of clinical images referencing body parts varied, with 49.8% of those images focusing on the head and neck region, 15.3% focusing on the thorax, and 13.8% focusing on the lower extremities. CONCLUSION: The diversity of image types, content, and uploaders within a home grown EHR system reflected the versatility and importance of the image uploading tool. Understanding how users utilize image uploading tools in a clinical setting highlights important considerations for designing better EHR tools and the importance of interoperability between EHR systems and other health technology.

A Systematic Intervention To Improve Serious Illness Communication In Primary Care.

Improving communication about goals and values for patients with advancing serious illness nearing the end of life is a key opportunity to improve the value of care. The Serious Illness Care Program, implemented at primary care clinics affiliated with Brigham and Women's Hospital in Boston, Massachusetts, is a multicomponent intervention designed to support best practices in communication by clinicians to increase conversations with patients with serious illness about their goals and values. We conducted a study of the program in fourteen primary care clinics participating in a high-risk care management program based in an accountable care organization. Patients in the clinics with the program implemented were more likely than those in comparison clinics to have serious illness conversations-including discussion of values and goals-documented in patients' medical records. Clinicians who participated also reported high satisfaction with training they received as part of the program, which they regarded as effective. This work suggests that the Serious Illness Care Program promotes more and better conversations among selected primary care patients, and it highlights the need for further research.

Assessing information system readiness for mitigating malpractice risk through simulation: results of a multi-site study.

OBJECTIVE: To develop and test an instrument for assessing a healthcare organization's ability to mitigate malpractice risk through clinical decision support (CDS). MATERIALS AND METHODS: Based on a previously collected malpractice data set, we identified common types of CDS and the number and cost of malpractice cases that might have been prevented through this CDS. We then designed clinical vignettes and questions that test an organization's CDS capabilities through simulation. Seven healthcare organizations completed the simulation. RESULTS: All seven organizations successfully completed the self-assessment. The proportion of potentially preventable indemnity loss for which CDS was available ranged from 16.5% to 73.2%. DISCUSSION: There is a wide range in organizational ability to mitigate malpractice risk through CDS, with many organizations' electronic health records only being able to prevent a small portion of malpractice events seen in a real-world dataset. CONCLUSION: The simulation approach to assessing malpractice risk mitigation through CDS was effective. Organizations should consider using malpractice claims experience to facilitate prioritizing CDS development.

Use of an electronic problem list by primary care providers and specialists.

BACKGROUND: Accurate patient problem lists are valuable tools for improving the quality of care, enabling clinical decision support, and facilitating research and quality measurement. However, problem lists are frequently inaccurate and out-of-date and use varies widely across providers. OBJECTIVE: Our goal was to assess provider use of an electronic problem list and identify differences in usage between medical specialties. DESIGN: Chart review of a random sample of 100,000 patients who had received care in the past two years at a Boston-based academic medical center. PARTICIPANTS: Counts were collected of all notes and problems added for each patient from 1/1/2002 to 4/30/2010. For each entry, the recording provider and the clinic in which the entry was recorded was collected. We used the Healthcare Provider Taxonomy Code Set to categorize each clinic by specialty. MAIN MEASURES: We analyzed the problem list use across specialties, controlling for note volume as a proxy for visits. KEY RESULTS: A total of 2,264,051 notes and 158,105 problems were recorded in the electronic medical record for this population during the study period. Primary care providers added 82.3% of all problems, despite writing only 40.4% of all notes. Of all patients, 49.1% had an assigned primary care provider (PCP) affiliated with the hospital; patients with a PCP had an average of 4.7 documented problems compared to 1.5 problems for patients without a PCP. CONCLUSIONS: Primary care providers were responsible for the majority of problem documentation; surgical and medical specialists and subspecialists recorded a disproportionately small number of problems on the problem list.

Improving completeness of electronic problem lists through clinical decision support: a randomized, controlled trial.

BACKGROUND: Accurate clinical problem lists are critical for patient care, clinical decision support, population reporting, quality improvement, and research. However, problem lists are often incomplete or out of date. OBJECTIVE: To determine whether a clinical alerting system, which uses inference rules to notify providers of undocumented problems, improves problem list documentation. STUDY DESIGN AND METHODS: Inference rules for 17 conditions were constructed and an electronic health record-based intervention was evaluated to improve problem documentation. A cluster randomized trial was conducted of 11 participating clinics affiliated with a large academic medical center, totaling 28 primary care clinical areas, with 14 receiving the intervention and 14 as controls. The intervention was a clinical alert directed to the provider that suggested adding a problem to the electronic problem list based on inference rules. The primary outcome measure was acceptance of the alert. The number of study problems added in each arm as a pre-specified secondary outcome was also assessed. Data were collected during 6-month pre-intervention (11/2009-5/2010) and intervention (5/2010-11/2010) periods. RESULTS: 17,043 alerts were presented, of which 41.1% were accepted. In the intervention arm, providers documented significantly more study problems (adjusted OR=3.4, p<0.001), with an absolute difference of 6277 additional problems. In the intervention group, 70.4% of all study problems were added via the problem list alerts. Significant increases in problem notation were observed for 13 of 17 conditions. CONCLUSION: Problem inference alerts significantly increase notation of important patient problems in primary care, which in turn has the potential to facilitate quality improvement. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01105923.

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.

BACKGROUND: Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. OBJECTIVE: To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. STUDY DESIGN AND METHODS: We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. RESULTS: Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. CONCLUSION: We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

Clinician attitudes toward and use of electronic problem lists: a thematic analysis.

BACKGROUND: The clinical problem list is an important tool for clinical decision making, quality measurement and clinical decision support; however, problem lists are often incomplete and provider attitudes towards the problem list are poorly understood. METHODS: An ethnographic study of healthcare providers conducted from April 2009 to January 2010 was carried out among academic and community outpatient medical practices in the Greater Boston area across a wide range of medical and surgical specialties. Attitudes towards the problem list were then analyzed using grounded theory methods. RESULTS: Attitudes were variable, and dimensions of variations fit into nine themes: workflow, ownership and responsibility, relevance, uses, content, presentation, accuracy, alternatives, support/education and one cross-cutting theme of culture. CONCLUSIONS: Significant variation was observed in clinician attitudes towards and use of the electronic patient problem list. Clearer guidance and best practices for problem list utilization are needed.

An automated technique for identifying associations between medications, laboratory results and problems.

BACKGROUND: The patient problem list is an important component of clinical medicine. The problem list enables decision support and quality measurement, and evidence suggests that patients with accurate and complete problem lists may have better outcomes. However, the problem list is often incomplete. OBJECTIVE: To determine whether association rule mining, a data mining technique, has utility for identifying associations between medications, laboratory results and problems. Such associations may be useful for identifying probable gaps in the problem list. DESIGN: Association rule mining was performed on structured electronic health record data for a sample of 100,000 patients receiving care at the Brigham and Women's Hospital, Boston, MA. The dataset included 272,749 coded problems, 442,658 medications and 11,801,068 laboratory results. MEASUREMENTS: Candidate medication-problem and laboratory-problem associations were generated using support, confidence, chi square, interest, and conviction statistics. High-scoring candidate pairs were compared to a gold standard: the Lexi-Comp drug reference database for medications and Mosby's Diagnostic and Laboratory Test Reference for laboratory results. RESULTS: We were able to successfully identify a large number of clinically accurate associations. A high proportion of high-scoring associations were adjudged clinically accurate when evaluated against the gold standard (89.2% for medications with the best-performing statistic, chi square, and 55.6% for laboratory results using interest). CONCLUSION: Association rule mining appears to be a useful tool for identifying clinically accurate associations between medications, laboratory results and problems and has several important advantages over alternative knowledge-based approaches.

USB-based Personal Health Records: an analysis of features and functionality.

PURPOSE: To determine the features of commercially available USB-based Personal Health Records (PHR) devices, and compare the commercial state of the art to recommendations made by certification committees. METHODS: Thirteen USB-based PHRs were identified and analyzed based on data elements used and features provided. Marketing techniques used by the companies were also explored. RESULTS: Eight of the thirteen PHRs contained all seven clinical data elements (problems, procedures, medications, providers, allergies, labs, immunizations), three were missing a single element and the remaining two lacked two elements. In the features analysis no single PHR contained all eight features (export data, import data, images, summary print out, emergency entry, teaching material available for problem, username and password supported, Mac-compatible), but two of the devices had seven of the eight features. Finally, scare tactics were used in marketing all but two of the PHR devices. CONCLUSION: While PHRs are very important in the health care field, at the present time, USB-based PHRs currently on the market appear to have deficiencies. Tethered or web-based PHRs may be a better option for consumers at present.

Laying the groundwork for enterprise-wide medical language processing services: architecture and process.

A systematic and standard process for capturing information within free-text clinical documents could facilitate opportunities for improving quality and safety of patient care, enhancing decision support, and advancing data warehousing across an enterprise setting. At Partners HealthCare System, the Medical Language Processing (MLP) services project was initiated to establish a component-based architectural model and processes to facilitate putting MLP functionality into production for enterprise consumption, promote sharing of components, and encourage reuse. Key objectives included exploring the use of an open-source framework called the Unstructured Information Management Architecture (UIMA) and leveraging existing MLP-related efforts, terminology, and document standards. This paper describes early experiences in defining the infrastructure and standards for extracting, encoding, and structuring clinical observations from a variety of clinical documents to serve enterprise-wide needs.

Enhancing clinical problem lists through data mining and natural language processing.

The availability of timely, accurate, comprehensive, and coded clinical problem lists is essential for supporting a range of healthcare activities. Evidence and experience suggest, however, that problem lists are frequently out-of-date, sometimes omit clinically important problems, and contain uncoded entries. Here, we describe a study being performed at Partners HealthCare System to explore automated techniques for enhancing existing problem lists.