Identifying Outcomes of Care from Medical Records to Improve Doctor-Patient Communication

Between appointments, healthcare providers have limited interaction with their patients, but patients have similar patterns of care. Medications have common side effects; injuries have an expected healing time; and so on. By modeling patient interventions with outcomes, healthcare systems can equip providers with better feedback. In this work, we present a pipeline for analyzing medical records according to an ontology directed at allowing closed-loop feedback between medical encounters. Working with medical data from multiple domains, we use a combination of data processing, machine learning, and clinical expertise to extract knowledge from patient records. While our current focus is on technique, the ultimate goal of this research is to inform development of a system using these models to provide knowledge-driven clinical decision-making

Network and systems medicine: Position paper of the European Collaboration on Science and Technology action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management

What are we missing by ignoring text records in the Clinical Practice Research Datalink? Using three symptoms of cancer as examples to estimate the extent of data in text format that is hidden to research

Electronic medical record databases (e.g. the Clinical Practice Research Datalink, CPRD) are increasingly used in epidemiological research. The CPRD has two formats of data: coded, which is the sole format used in almost all research; and free-text (or ‘hidden’), which may contain much clinical information but is generally unavailable to researchers. This thesis examines the ramifications of omitting free-text records from research. Cases with bladder (n=4,915) or pancreatic (n=3,635) cancer were matched to controls (n=21,718, bladder; n=16,459, pancreas) on age, sex and GP practice. Coded and text-only records of attendance for haematuria, jaundice and abdominal pain in the year before cancer diagnosis were identified. The number of patients whose entire attendance record for a symptom/sign existed solely in the text was quantified. Associations between recording method (coded or text-only) and case/control status were estimated (χ2 test). For each symptom/sign, the positive predictive value (PPV, Bayes' Theorem) and odds ratio (OR, conditional logistic regression) for cancer were estimated before and after supplementation with text-only records. Text-only recording was considerable, with 7,951/20,958 (37%) of symptom records being in that format. For individual patients, text-only recording was more likely in controls (140/336=42%) than cases (556/3,147=18%) for visible haematuria in bladder cancer (χ2 test, p<0.001), and for jaundice (21/31=67% vs 463/1,565=30%, p<0.0001) and abdominal pain (323/1,126=29% vs 397/1,789=22%, p<0.001) in pancreatic cancer. Adding text records reduced PPVs of visible haematuria for bladder cancer from 4.0% (95% CI: 3.5–4.6%) to 2.9% (2.6–3.2%) and of jaundice for pancreatic cancer from 12.8% (7.3–21.6%) to 6.3% (4.5–8.7%). Coded records suggested that non-visible haematuria occurred in 127/4,915 (2.6%) cases, a figure below that generally used for study. Supplementation with text-only records increased this to 312/4,915 (6.4%), permitting the first estimation of its OR (28.0, 95% CI: 20.7–37.9, p<0.0001) and PPV (1.60%, 1.22–2.10%, p<0.0001) for bladder cancer. The results suggest that GPs make strong clinical judgements about the probable significance of symptoms – preferentially coding clinical features they consider significant to a diagnosis, while using text to record those that they think are not

Implementation of data management and effect on chronic disease coding in a primary care organisation: A parallel cohort observational study

Background Consistent and standardized coding for chronic conditions is associated with better care; however, coding may currently be limited in electronic medical records (EMRs) used in Canadian primary care.Objectives To implement data management activities in a community-based primary care organisation and to evaluate the effects on coding for chronic conditions.Methods Fifty-nine family physicians in Toronto, Ontario, belonging to a single primary care organisation, participated in the study. The organisation implemented a central analytical data repository containing their EMR data extracted, cleaned, standardized and returned by the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), a large validated primary care EMR-based database. They used reporting software provided by CPCSSN to identify selected chronic conditions and standardized codes were then added back to the EMR. We studied four chronic conditions (diabetes, hypertension, chronic obstructive pulmonary disease and dementia). We compared changes in coding over six months for physicians in the organisation with changes for 315 primary care physicians participating in CPCSSN across Canada.Results Chronic disease coding within the organisation increased significantly more than in other primary care sites. The adjusted difference in the increase of coding was 7.7% (95% confidence interval 7.1%–8.2%, p < 0.01). The use of standard codes, consisting of the most common diagnostic codes for each condition in the CPCSSN database, increased by 8.9% more (95% CI 8.3%–9.5%, p < 0.01).Conclusions Data management activities were associated with an increase in standardized coding for chronic conditions. Exploring requirements to scale and spread this approach in Canadian primary care organisations may be worthwhile