A rapid and accurate diagnosis of Clostridium difficile infection (CDI) is essential for patient management and implementation of infection control measures. During a prospective time-series study, we compared the impact of three different diagnostic strategies on patient care. Each strategy was tested during a 3-month period: P1 (diagnosis based on the stool cytotoxicity assay and the toxigenic culture), P2 (diagnosis based on PCR) and P3 (two-step algorithm based on glutamate dehydrogenase detection followed by nucleic acid amplification test). The following criteria were used to assess the quality of patient management: (i) time for result reporting, (ii) frequency of repeat testing within 7 days, (iii) time elapsed between stool collection and beginning of treatment for patients with CDI, and (iv) frequency of empirical treatment for patients without CDI. Of 1122 stool samples (P1 n = 359, P2 n = 374, P3 n = 389), 36 (10.0%), 47 (12.3%) and 48 (12.3%) were positive for C. difficile during P1, P2 and P3, respectively. The time for reporting of a positive or a negative result was significantly shorter and the frequency of redundant stool samples within 7 days was lower during P2 and P3 than during P1. Patients with CDI were specifically treated with vancomycin or metronidazole earlier during P2 and P3 than patients from P1 (0.5 ± 0.5 days and 1.0 ± 1.8 days vs. 2.0 ± 1.7 days). The empirical therapy among patients without CDI decreased from 13.6% during P1 to 6.4% during P2 and 5.6% during P3. A rapid CDI diagnosis impacts positively on patient care.

The convergence of translational genomics and biomedical informatics has changed healthcare delivery. Institutional consortia have begun implementing lab testing and decision support for drug–gene interactions. Aggregate datasets are now revealing the impact of clinical decision support for drug–gene interactions. Given the pleiotropic nature of pharmacogenes, interdisciplinary teams and robust clinical decision support tools must exist within an informatics framework built to be flexible and capable of cross-talk between clinical specialties. Navigation of the challenges presented with the implementation of five steps to build a genetics program infrastructure requires the expertise of multiple healthcare professionals. Ultimately, this manuscript describes our efforts to place pharmacogenomics in the hands of the primary care provider integrating this information into a patient’s healthcare over their lifetime.

Clinical manifestations of biological aging can be remarkably similar to the side effects of frequently used medications. Fatigue, muscle pain, and confusion are common and often not shared as part of proper geriatric patient history. When patients report them, a root cause is usually confounding. These symptoms not only negatively impact health and wellness outcomes, patient quality of life, and increase costs to the healthcare system, but also may be a limitation on provider best practices. The patient, a 71-year-old female of European descent, enrolled in pharmacogenomics-enriched comprehensive medication management (PGx+CMM) program through her retirement benefit. At the time of testing, she was approximately 18 months post cerebrovascular accident and was being observed by her primary care provider for common chronic conditions. Of interest, she had been manifesting unreported clinical symptoms of fatigue, hypotension, and myalgia. Addressing these patient concerns and specifically focusing on an individual's goals, fears, and basic needs, rather than concentrating merely on the absence of disease, is the crux of personalized medicine and programs that address the notion of healthy aging. The patient's therapeutic regimen was adjusted based on PGx+CMM pharmacist review, use of a clinical decision support system (CDSS), and communication of recommendations to the prescribing physician. The patient saw rapid improvements in symptoms, suggesting they were caused by medication side effects. Her blood pressure and cholesterol levels remained controlled while noticeable side effects were eliminated. This case study demonstrates the positive impacts of personalized medicine and shows how pharmacists can be empowered with a CDSS to positively impact healthcare.