Pharmacogenomic testing and prescribing patterns for patients with cancer in a large national precision medicine cohort.

Population databases could help patients with cancer and providers better understand current pharmacogenomic prescribing and testing practices. This retrospective observational study analysed patients with cancer, drugs with pharmacogenomic evidence and related genetic testing in the National Institutes of Health All of Us database. Most patients with cancer (19 633 (88.3%) vs 2590 (11.7%)) received ≥1 drug and 36 (0.2%) received genetic testing, with a significant association between receiving ≥1 drug and age group (p<0.001), but not sex (p=0.612), race (p=0.232) or ethnicity (p=0.971). Drugs with pharmacogenomic evidence-but not genetic testing-were common for patients with cancer, reflecting key gaps preventing precision medicine from becoming standard of care.

Precision Medicine Landscape of Genomic Testing for Patients With Cancer in the National Institutes of Health All of Us Database Using Informatics Approaches.

PURPOSE: The rapid growth of biomedical data ecosystems has catalyzed research for oncology and precision medicine. We leverage federal cloud-based precision medicine databases and tools to better understand the current landscape of precision medicine and genomic testing for patients with cancer. METHODS: Retrospective observational study of genomic testing for patients with cancer in the National Institutes of Health All of Us Research Program, with the cancer cohort defined as having at least two documented or reported cancer diagnoses. RESULTS: There were 5,678 (1.8%) All of Us participants in the cancer cohort, with a significant difference between cancer status by age category, sex, race, and ethnicity (P < .001 for all). There were 295 (5.2%) patients with cancer who received genomic testing compared with 6,734 (2.2%) of noncancer patients, with 752 genomic tests commonly focused on gene mutations (primarily pharmacogenomics), molecular pathology, or clinical cytogenetic reports. CONCLUSION: Although not yet ubiquitous, diverse clinical genomic analyses in oncology can set the stage to grow the practice of precision medicine by integrating research patient data repositories, cancer data ecosystems, and biomedical informatics.

Practical Aspects of Implementing and Applying Health Care Cloud Computing Services and Informatics to Cancer Clinical Trial Data.

PURPOSE: Cloud computing has led to dramatic growth in the volume, variety, and velocity of cancer data. However, cloud platforms and services present new challenges for cancer research, particularly in understanding the practical tradeoffs between cloud performance, cost, and complexity. The goal of this study was to describe the practical challenges when using a cloud-based service to improve the cancer clinical trial matching process. METHODS: We collected information for all interventional cancer clinical trials from ClinicalTrials.gov and used the Google Cloud Healthcare Natural Language Application Programming Interface (API) to analyze clinical trial Title and Eligibility Criteria text. An informatics pipeline leveraging interoperability standards summarized the distribution of cancer clinical trials, genes, laboratory tests, and medications extracted from cloud-based entity analysis. RESULTS: There were a total of 38,851 cancer-related clinical trials found in this study, with the distribution of cancer categories extracted from Title text significantly different than in ClinicalTrials.gov (P < .001). Cloud-based entity analysis of clinical trial criteria identified a total of 949 genes, 1,782 laboratory tests, 2,086 medications, and 4,902 National Cancer Institute Thesaurus terms, with estimated detection accuracies ranging from 12.8% to 89.9%. A total of 77,702 API calls processed an estimated 167,179 text records, which took a total of 1,979 processing-minutes (33.0 processing-hours), or approximately 1.5 seconds per API call. CONCLUSION: Current general-purpose cloud health care tools-like the Google service in this study-should not be used for automated clinical trial matching unless they can perform effective extraction and classification of the clinical, genetic, and medication concepts central to precision oncology research. A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research.

Using informatics to guide public health policy during the COVID-19 pandemic in the USA.

BACKGROUND: Current and future pandemics will require informatics solutions to assess the risks, resources and policies to guide better public health decision-making. METHODS: Cross-sectional study of all COVID-19 cases and deaths in the USA on a population- and resource-adjusted basis (as of 24 April 2020) by applying biomedical informatics and data visualization tools to several public and federal government datasets, including analysis of the impact of statewide stay-at-home orders. RESULTS: There were 2753.2 cases and 158.0 deaths per million residents, respectively, in the USA with variable distributions throughout divisions, regions and states. Forty-two states and Washington, DC, (84.3%) had statewide stay-at-home orders, with the remaining states having population-adjusted characteristics in the highest risk quartile. CONCLUSIONS: Effective national preparedness requires clearly understanding states' ability to predict, manage and balance public health needs through all stages of a pandemic. This will require leveraging data quickly, correctly and responsibly into sound public health policies.

Assessing the readiness of precision medicine interoperabilty: An exploratory study of the National Institutes of Health genetic testing registry.

BACKGROUND:   Precision medicine involves three major innovations currently taking place in healthcare:  electronic health records, genomics, and big data.  A major challenge for healthcare providers, however, is understanding the readiness for practical application of initiatives like precision medicine. OBJECTIVE:   To better understand the current state and challenges of precision medicine interoperability using a national genetic testing registry as a starting point, placed in the context of established interoperability formats. METHODS:   We performed an exploratory analysis of the National Institutes of Health Genetic Testing Registry.  Relevant standards included Health Level Seven International Version 3 Implementation Guide for Family History, the Human Genome Organization Gene Nomenclature Committee (HGNC) database, and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT).  We analyzed the distribution of genetic testing laboratories, genetic test characteristics, and standardized genome/clinical code mappings, stratified by laboratory setting. RESULTS: There were a total of 25472 genetic tests from 240 laboratories testing for approximately 3632 distinct genes.  Most tests focused on diagnosis, mutation confirmation, and/or risk assessment of germline mutations that could be passed to offspring.  Genes were successfully mapped to all HGNC identifiers, but less than half of tests mapped to SNOMED CT codes, highlighting significant gaps when linking genetic tests to standardized clinical codes that explain the medical motivations behind test ordering.  Conclusion:  While precision medicine could potentially transform healthcare, successful practical and clinical application will first require the comprehensive and responsible adoption of interoperable standards, terminologies, and formats across all aspects of the precision medicine pipeline.

Sex-specific patterns and differences in dementia and Alzheimer's disease using informatics approaches.

The National Institutes of Health Office of Research on Women's Health recently highlighted the critical need for explicitly addressing sex differences in biomedical research, including Alzheimer's disease and dementia. The purpose of our study was to perform a sex-stratified analysis of cognitive impairment using diverse medical, clinical, and genetic factors of unprecedented scale and scope by applying informatics approaches to three large Alzheimer's databases. Analyses suggested females were 1.5 times more likely than males to have a documented diagnosis of probable Alzheimer's disease, and several other factors fell along sex-specific lines and were possibly associated with severity of cognitive impairment.

Genetic testing behavior and reporting patterns in electronic medical records for physicians trained in a primary care specialty or subspecialty.

OBJECTIVE: To characterize important patterns of genetic testing behavior and reporting in modern electronic medical records (EMRs) at the institutional level. MATERIALS AND METHODS: Retrospective observational study using EMR data of all 10,715 patients who received genetic testing by physicians trained in a primary care specialty or subspecialty at an academic medical center between January 1, 2008 and December 31, 2010. RESULTS: Patients had a mean±SD age of 38.3±15.8 years (median 36.1, IQR 30.0-43.8). The proportion of female subjects in the study population was larger than in the general patient population (77.2% vs 55.0%, p<0.001) and they were younger than the male subjects in the study (36.5±13.2 vs 44.6±21.2 years, p<0.001). Approximately 1.1% of all patients received genetic testing. There were 942 physicians who ordered a total of 15,320 genetic tests. By volume, commonly tested genes involved mutations for cystic fibrosis (36.7%), prothrombin (13.7%), Tay-Sachs disease (6.7%), hereditary hemochromatosis (4.4%), and chronic myelogenous leukemia (4.1%). EMRs stored reports as free text with categorical descriptions of mutations and an average length of 269.4±153.2 words (median 242, IQR 146-401). CONCLUSIONS: In this study, genetic tests were often ordered by a diverse group of physicians for women of childbearing age being evaluated for diseases that may affect potential offspring. EMRs currently serve primarily as a storage warehouse for textual reports that could potentially be transformed into meaningful structured data for next-generation clinical decision support. Further studies are needed to address the design, development, and implementation of EMRs capable of managing the critical genetic health information challenges of the future.

Population-based breast cancer screening in a primary care network.

OBJECTIVES: To assess the ability of a health information technology system to facilitate population- based breast cancer screening. STUDY DESIGN: Cohort study with 2-year follow-up after a 1-year cluster randomized trial. METHODS: Study population was women 42 to 69 years old receiving care within a 12-practice primary care network. The management informatics system (1) identified women overdue for mammograms, (2) connected them to primary care providers using a web-based tool, (3) created automatically generated outreach letters for patients specified by providers, (4) monitored for subsequent mammography scheduling and completion, and (5) provided practice delegates with a list of women remaining unscreened for reminder phone calls. Eligible women overdue for a mammogram during a 1-year study period included those overdue at study start (prevalent cohort) and those who became overdue during follow-up (incident cohort). The main outcome measure was mammography completion rates over 3 years. RESULTS: Among 32,688 eligible women, 9795 (30%) were overdue for screening (4487 intervention, 5308 control). Intervention patients were somewhat younger, more likely to be non-Hispanic white, and more likely to have health insurance compared with control patients. Adjusted completion rates in the prevalent cohort (n = 6697) were significantly higher among intervention patients after 3 years (51.7% vs 45.8%; P = .002). For patients in the incident cohort (n = 3098), adjusted completion rates after 2 years were 53.8% versus 48.7%, respectively (P = .052). CONCLUSIONS: Population-based informatics systems can enable sustained increases in mammography screening rates beyond rates seen with office-based visit reminders.

A cluster-randomized trial of a primary care informatics-based system for breast cancer screening.

BACKGROUND: Information technology offers the promise, as yet unfulfilled, of delivering efficient, evidence-based health care. OBJECTIVE: To evaluate whether a primary care network-based informatics intervention can improve breast cancer screening rates. DESIGN: Cluster-randomized controlled trial of 12 primary care practices conducted from March 20, 2007 to March 19, 2008. PATIENTS: Women 42-69 years old with no record of a mammogram in the prior 2 years. INTERVENTIONS: In intervention practices, a population-based informatics system was implemented that: connected overdue patients to appropriate care providers, presented providers with a Web-based list of their overdue patients in a non-visit-based setting, and enabled "one-click" mammography ordering or documented deferral reasons. Patients selected for mammography received automatically generated letters and follow-up phone calls. All practices had electronic health record reminders about breast cancer screening available during clinical encounters. MAIN MEASURES: The primary outcome was the proportion of overdue women undergoing mammography at 1-year follow-up. KEY RESULTS: Baseline mammography rates in intervention and control practices did not differ (79.5% vs 79.3%, p = 0.73). Among 3,054 women in intervention practices and 3,676 women in control practices overdue for mammograms, intervention patients were somewhat younger, more likely to be non-Hispanic white, and have health insurance. Most intervention providers used the system (65 of 70 providers, 92.9%). Action was taken for 2,652 (86.8%) intervention patients [2,274 (74.5%) contacted and 378 (12.4%) deferred]. After 1 year, mammography rates were significantly higher in the intervention arm (31.4% vs 23.3% in control arm, p < 0.001 after adjustment for baseline differences; 8.1% absolute difference, 95% CI 5.1-11.2%). All demographic subgroups benefited from the intervention. Intervention patients completed screening sooner than control patients (p < 0.001). CONCLUSIONS: A novel population-based informatics system functioning as part of a non-visit-based care model increased mammography screening rates in intervention practices. TRIAL REGISTRATION: ClinicalTrials.gov; NCT00462891.

A Clinical Database-Driven Approach to Decision Support: Predicting Mortality Among Patients with Acute Kidney Injury.

In exploring an approach to decision support based on information extracted from a clinical database, we developed mortality prediction models of intensive care unit (ICU) patients who had acute kidney injury (AKI) and compared them against the Simplified Acute Physiology Score (SAPS). We used MIMIC, a public de-identified database of ICU patients admitted to Beth Israel Deaconess Medical Center, and identified 1400 patients with an ICD9 diagnosis of AKI and who had an ICU stay > 3 days. Multivariate regression models were built using the SAPS variables from the first 72 hours of ICU admission. All the models developed on the training set performed better than SAPS (AUC = 0.64, Hosmer-Lemeshow p < 0.001) on an unseen test set; the best model had an AUC = 0.74 and Hosmer-Lemeshow p = 0.53. These findings suggest that local customized modeling might provide more accurate predictions. This could be the first step towards an envisioned individualized point-of-care probabilistic modeling using one's clinical database.

Mammography FastTrack: an intervention to facilitate reminders for breast cancer screening across a heterogeneous multi-clinic primary care network.

Health care information technology can be a means to improve quality and efficiency in the primary care setting. However, merely applying technology without addressing how it fits into provider workflow and existing systems is unlikely to achieve improvement goals. Improving quality of primary care, such as cancer screening rates, requires addressing barriers at system, provider, and patient levels. The authors report the development, implementation, and preliminary use of a new breast cancer screening outreach program in a large multicenter primary care network. This installation paired population-based surveillance with customized information delivery based on a validated model linking patients to providers and practices. In the first six months, 86% of physicians and all case managers voluntarily participated in the program. Providers intervened in 83% of the mammogram-overdue population by initiating mailed reminders or deferring contact. Overall, 63% of patients were successfully contacted. Systematic population-based efforts are promising tools to improve preventative care.

Designing healthcare information technology to catalyse change in clinical care.

The gap between best practice and actual patient care continues to be a pervasive problem in our healthcare system. Efforts to improve on this knowledge-performance gap have included computerised disease management programs designed to improve guideline adherence. However, current computerised reminder and decision support interventions directed at changing physician behaviour have had only a limited and variable effect on clinical outcomes. Further, immediate pay-for-performance financial pressures on institutions have created an environment where disease management systems are often created under duress, appended to existing clinical systems and poorly integrated into the existing workflow, potentially limiting their real-world effectiveness. The authors present a review of disease management as well as a conceptual framework to guide the development of more effective health information technology (HIT) tools for translating clinical information into clinical action.

Lessons from implementing a combined workflow-informatics system for diabetes management.

Shortcomings surrounding the care of patients with diabetes have been attributed largely to a fragmented, disorganized, and duplicative health care system that focuses more on acute conditions and complications than on managing chronic disease. To address these shortcomings, we developed a diabetes registry population management application to change the way our staff manages patients with diabetes. Use of this new application has helped us coordinate the responsibilities for intervening and monitoring patients in the registry among different users. Our experiences using this combined workflow-informatics intervention system suggest that integrating a chronic disease registry into clinical workflow for the treatment of chronic conditions creates a useful and efficient tool for managing disease.

Diabetes information technology: designing informatics systems to catalyze change in clinical care.

Current computerized reminder and decision support systems intended to improve diabetes care have had a limited effect on clinical outcomes. Increasing pressures on health care networks to meet standards of diabetes care have created an environment where information technology systems for diabetes management are often created under duress, appended to existing clinical systems, and poorly integrated into the existing workflow. After defining the components of diabetes disease management, the authors present an eight-step conceptual framework to guide the development of more effective diabetes information technology systems for translating clinical information into clinical action.

How doctors choose medications to treat type 2 diabetes: a national survey of specialists and academic generalists.

OBJECTIVE: Glycemic control remains suboptimal despite the wide range of available medications. More effective medication prescription might result in better control. However, the process by which physicians choose glucose-lowering medicines is poorly understood. We sought to study the means by which physicians choose medications for type 2 diabetic patients. RESEARCH DESIGN AND METHODS: We surveyed 886 physician members of either the Society of General Internal Medicine (academic generalists, response rate 30%) or the American Diabetes Association (specialists, response rate 23%) currently managing patients with type 2 diabetes. Respondents weighed the importance of 15 patient, physician, and nonclinical factors when deciding which medications to prescribe for type 2 diabetic subjects at each of three management stages (initiation, use of second-line oral agents, and insulin). RESULTS: Respondents reported using a median of five major considerations (interquartile range 4-6) at each stage. Frequently cited major considerations included overall assessment of the patient's health/comorbidity, A1C level, and patient's adherence behavior but not expert guidelines/hospital algorithms or patient age. For insulin initiation, academic generalists placed greater emphasis on patient adherence (76 vs. 60% of specialists, P < 0.001). These generalists also identified patient fear of injections (68%) and patient desire to prolong noninsulin therapy (68%) as major insulin barriers. Overall, qualitative factors (e.g., adherence, motivation, overall health assessment) were somewhat more highly considered than quantitative factors (e.g., A1C, age, weight) with mean aggregate scores of 7.3 vs. 6.9 on a scale of 0-10, P < 0.001. CONCLUSIONS: The physicians in our survey considered a wide range of qualitative and quantitative factors when making medication choices for hyperglycemia management. The apparent complexity of the medication choice process contrasts with current evidence-based treatment guidelines.

Facilitating group learning and dialogue between neighboring academic institutions using a collaborative web application framework.

We describe a web-based academic portal designed to promote both synchronous and asynchronous information exchange among biomedical informaticians in the Greater Boston area. The system facilitates academic interaction through several collaborative online applications including wikis, blogs, group-calendars, group-announcements, group-forums, and shared file-storage. This infrastructure provides a means for users to contribute and tag content and to receive alerts as content of interest emerges. This framework helps facilitate group learning and dialogue among geographically distinct facilities.

Quicksilver: a novel publish and subscribe clinical messaging platform.

The authors introduce and describe the features of Quicksilver, a novel clinical messaging platform deployed in a multidisciplinary academic primary care clinic. The system follows a publication-subscription messaging paradigm employing dynamic role-based addressing. Quicksilver leverages the open-source XMPP, a powerful and extensible protocol for efficient asynchronous and synchronous communication commonly used in instant messaging applications.

New models of population management for patients with diabetes--using informatics tools to support primary care.

Diabetes management continues to fall short of evidence-based goals of care. Population management represents a new approach to diabetes care for large numbers of patients with diabetes cared for within a single clinical system. This method is information intensive and generally requires an advanced informatics infrastructure. While Information Processing is a critical first step in population management, to have a significant impact on disease control population-based intervention must also employ potent Clinical Action tools that lower barriers to effective care. In this review we present two recent population management interventions within our health system that illustrate the principles of Information Processing and Clinical Action in diabetes care.

Randomized controlled trial of an informatics-based intervention to increase statin prescription for secondary prevention of coronary disease.

OBJECTIVE: Suboptimal treatment of hyperlipidemia in patients with coronary artery disease (CAD) is well documented. We report the impact of a computer-assisted physician-directed intervention to improve secondary prevention of hyperlipidemia. DESIGN AND SETTING: Two hundred thirty-five patients under the care of 14 primary care physicians in an academically affiliated practice with an electronic health record were enrolled in this proof-of-concept physician-blinded randomized, controlled trial. Each patient with CAD or risk equivalent above National Cholesterol Education Program-recommended low-density lipoprotein (LDL) treatment goal for greater than 6 months was randomized, stratified by physician and baseline LDL. Physicians received a single e-mail per intervention patient. E-mails were visit independent, provided decision support, and facilitated "one-click" order writing. MEASUREMENTS: The primary outcomes were changes in hyperlipidemia prescriptions, time to prescription change, and changes in LDL levels. The time spent using the system was assessed among intervention patients. RESULTS: A greater proportion of intervention patients had prescription changes at 1 month (15.3% vs 2%, P=.001) and 1 year (24.6% vs 17.1%, P=.14). The median interval to first medication adjustment occurred earlier among intervention patients (0 vs 7.1 months, P=.005). Among patients with baseline LDLs >130 mg/dL, the first postintervention LDLs were substantially lower in the intervention group (119.0 vs 138.0 mg/dL, P=.04). Physician processing time was under 60 seconds per e-mail. CONCLUSION: A visit-independent disease management tool resulted in significant improvement in secondary prevention of hyperlipidemia at 1-month postintervention and showed a trend toward improvement at 1 year.

Facilitated lipid management using interactive e-mail: preliminary results of a randomized controlled trial.

We describe the design and implementation of a clinical decision support system for ambulatory hyperlipidemia management. Despite readily available and evidence-based guidelines, cholesterol control remains persistently sub-optimal in clinical practice. "FastTrack" is an integrated, multi-faceted informatics tool to reduce barriers to effective cholesterol management by proactively integrating pertinent clinical information, evidence-based decision support and a simple means to act via a single FastTrack e-mail. We tested the intervention in a randomized, controlled trial involving 14 physicians and 276 patients. In an interim analysis, we found that physicians on average were able to review and act upon 9 patient e-mails in less than 15 minutes. Significantly more intervention patients were initiated on a statin therapy or had their dose increased (15.3% vs. 2.3%, p < 0.001). Impact of the intervention on LDL levels will be assessed at trial completion.