The goal of personalized health care is to make patient data available at the right time, in the right format and within the normal workflow [1
]. The objective is to support providers, decrease time spent in gathering data and improve the quality of care for patients. While this is important for all patients, it is especially critical for sub-specialty patients with complex medical conditions and longitudinal data. The availability and easy access to these data are important for the sub-specialty provider for routine patient care and for others called upon to provide crosscoverage or emergency care [2
Clinical laboratories generate and archive patient data in laboratory information management systems (LIMS). These data include biochemical manifestations of organisms (phenotypes), genotypes and biomarkers that are essential to the diagnosis, determination of progression and response to therapy of a disease. These data form the foundation for clinical care, personalized medicine and translational research from an informatics standpoint of data capture, organization, integrity and flow [3
]. Where available, laboratory data are integrated into electronic medical records and are accessed routinely by medical personnel. Longitudinal or serial laboratory tests are usually viewed graphically or in tabular form via summary reports or trends [4
]. These reports are useful for visualizing structured data in the form of numeric values with flags for abnormal values. They are less optimal for data that are available only in unstructured free text in written reports or pictorially represented in graphs and gels from molecular genetics, anatomic pathology and other specialized testing such as protein immunology.
The medical care of patients in fields such as transplant medicine, cancer, HIV/AIDS, etc. generates large volumes of data including critically important serial laboratory data. Currently, there are no readily available composite laboratory data reports that incorporate both structured and unstructured elements for use in the care of such complex medical patients. Prior informatics work [5
] and our clinical partners have indicated that such automated reports would be useful in improving care while reducing time in gathering and collating these results manually.
Using multiple myeloma as a model disease, this pilot project addresses the hypotheses that: (1
) Clinical providers perceive composite laboratory reports to be important for the care of complex patients and (2
) Such reports can be generated using laboratory informatics methods. Multiple myeloma (MM) is the second most common cancer of the blood in which antibody producing plasma cells become malignant [10
]. Routine clinical care of these patients involves a time-consuming data gathering phase where the results of a battery of immunology tests, biomarkers and more recently, gene expression data are accessed and collated to assess progression of disease and response to therapy [11