Clinical Decision Support for Nurses: A Fall Risk and Prevention Example.

Clinical decision support tools in electronic health records have demonstrated improvement with process measures and clinician performance, predominantly for providers. Clinical decision support tools could improve patient fall risk identification and prevention plans, a common concern for nursing. This quality-improvement project used clinical decision support to improve the rate of nurse compliance with documented fall risk assessments and, for patients at high risk, fall prevention plans of care in 16 adult inpatient units. Preintervention and postintervention data were compared using quarterly audits, retrospective chart review, safety reports, and falls and falls-with-injury rates. Documentation of fall risk assessments on the 16 units improved significantly according to quarterly audit data (P = .05), whereas documentation of the plans of care did not. Retrospective chart review on two units indicated improvement for admission fall risk assessment (P = .05) and a decrease in the documentation of the shift plan of care (P = .01); one unit had a statistically significant decrease in documentation of plans of care on admission (P = .00). Examination of safety reports for patients who fell showed all patients before and after clinical decision support had fall risk assessments documented. Falls and falls with injury did not change significantly before and after clinical decision support intervention.

Modular design, application architecture, and usage of a self-service model for enterprise data delivery: the Duke Enterprise Data Unified Content Explorer (DEDUCE).

PURPOSE: Data generated in the care of patients are widely used to support clinical research and quality improvement, which has hastened the development of self-service query tools. User interface design for such tools, execution of query activity, and underlying application architecture have not been widely reported, and existing tools reflect a wide heterogeneity of methods and technical frameworks. We describe the design, application architecture, and use of a self-service model for enterprise data delivery within Duke Medicine. METHODS: Our query platform, the Duke Enterprise Data Unified Content Explorer (DEDUCE), supports enhanced data exploration, cohort identification, and data extraction from our enterprise data warehouse (EDW) using a series of modular environments that interact with a central keystone module, Cohort Manager (CM). A data-driven application architecture is implemented through three components: an application data dictionary, the concept of "smart dimensions", and dynamically-generated user interfaces. RESULTS: DEDUCE CM allows flexible hierarchies of EDW queries within a grid-like workspace. A cohort "join" functionality allows switching between filters based on criteria occurring within or across patient encounters. To date, 674 users have been trained and activated in DEDUCE, and logon activity shows a steady increase, with variability between months. A comparison of filter conditions and export criteria shows that these activities have different patterns of usage across subject areas. CONCLUSIONS: Organizations with sophisticated EDWs may find that users benefit from development of advanced query functionality, complimentary to the user interfaces and infrastructure used in other well-published models. Driven by its EDW context, the DEDUCE application architecture was also designed to be responsive to source data and to allow modification through alterations in metadata rather than programming, allowing an agile response to source system changes.

DEDUCE Clinical Text: An Ontology-based Module to Support Self-Service Clinical Notes Exploration and Cohort Development.

Large amounts of information, as well as opportunities for informing research, education, and operations, are contained within clinical text such as radiology reports and pathology reports. However, this content is less accessible and harder to leverage than structured, discrete data. We report on an extension to the Duke Enterprise Data Unified Content Explorer (DEDUCE), a self-service query tool developed to provide clinicians and researchers with access to data within the Duke Medicine Enterprise Data Warehouse (EDW). The DEDUCE Clinical Text module supports ontology-based text searching, enhanced filtering capabilities based on document attributes, and integration of clinical text with structured data and cohort development. The module is implemented with open-source tools extensible to other institutions, including a Java-based search engine (Apache Solr) with complementary full-text indexing library (Lucene) employed with a negation engine (NegEx) modified by clinical users to include to local domain-specific negation phrases.

Does access modality matter? Evaluation of validity in reusing clinical care data.

Self-service database portals may improve access to institutional data resources for clinical research or quality improvement, but questions remain about the validity of this approach. We tested the accuracy of data extracted from a clinical data repository using a self-service portal by comparing three approaches to measuring medication use among patients with coronary disease: (1) automated extraction using a portal, (2) extraction by an experienced data architect, and (3) manual chart abstraction. Outcomes included medications and diagnoses (e.g., myocardial infarction, heart failure). Charts were manually reviewed for 200 patients. Using matched criteria, self-service query identified 7327 of 7358 patients identified by the data analyst. For patients in both cohorts, agreement rates ranged from 0.99 for demographic data to 0.94 for laboratory data. Based on chart review, the self-service portal and the analyst had similar sensitivities and specificities for comorbid diagnoses and statin use.

Towards the creation of a flexible classification scheme for voluntarily reported transfusion and laboratory safety events.

BACKGROUND: Transfusion and clinical laboratory services are high-volume activities involving complicated workflows across both ambulatory and inpatient environments. As a result, there are many opportunities for safety lapses, leading to patient harm and increased costs. Organizational techniques such as voluntary safety event reporting are commonly used to identify and prioritize risk areas across care settings. Creation of functional, standardized safety data structures that facilitate effective exploratory examination is therefore essential to drive quality improvement interventions. Unfortunately, voluntarily reported adverse event data can often be unstructured or ambiguously defined. RESULTS: To address this problem, we sought to create a "best-of-breed" patient safety classification for data contained in the Duke University Health System Safety Reporting System (SRS). Our approach was to implement the internationally recognized World Health Organization International Classification for Patient Safety Framework, supplemented with additional data points relevant to our organization. Data selection and integration into the hierarchical framework is discussed, as well as placement of the classification into the SRS. We evaluated the impact of the new SRS classification on system usage through comparisons of monthly average report rates and completion times before and after implementation. Monthly average inpatient transfusion reports decreased from 102.1 ± 14.3 to 91.6 ± 11.2, with the proportion of transfusion reports in our system remaining consistent before and after implementation. Monthly average transfusion report rates in the outpatient and homecare environments were not significantly different. Significant increases in clinical lab report rates were present across inpatient and outpatient environments, with the proportion of lab reports increasing after implementation. Report completion times increased modestly but not significantly from a practical standpoint. CONCLUSIONS: A common safety vocabulary can facilitate integration of information from disparate systems and processes to permit meaningful measurement and interpretation of data to improve safety within and across organizations. Formation of a "best-of-breed" classification for voluntary reporting necessitates an internal examination of localized data needs and workflow in order to design a product that enables comprehensive data capture. A team of clinical, safety, and information technology experts is necessary to integrate the data structures into the reporting system. We have found that a "best-of-breed" patient safety classification provides a solid, extensible model for adverse event analysis, healthcare leader communication, and intervention identification.

The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN).

OBJECTIVE: Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials. Implementation of health-information technology has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities. DESIGN: Duke Medicine has deployed a hybrid solution, The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective Health Level 7 (HL7) messages to immediately alert study personnel of potential recruits as they become eligible. RESULTS: DISCERN analyzes more than 500000 messages daily in service of 12 projects. Users may receive results via email, text pages, or on-demand reports. Preliminary results suggest DISCERN's unique ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users. LIMITATIONS: The DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment. CONCLUSION: DISCERN's hybrid framework for identifying real-time clinical events housed in HL7 messages complements the traditional approach of using retrospective warehoused data. DISCERN is helpful in instances when the required clinical data may not be loaded into the warehouse and thus must be captured contemporaneously during patient care. Use of an open-source tool supports generalizability to other institutions at minimal cost.

Using a computerized provider order entry system to meet the unique prescribing needs of children: description of an advanced dosing model.

BACKGROUND: It is well known that the information requirements necessary to safely treat children with therapeutic medications cannot be met with the same approaches used in adults. Over a 1-year period, Duke University Hospital engaged in the challenging task of enhancing an established computerized provider order entry (CPOE) system to address the unique medication dosing needs of pediatric patients. METHODS: An advanced dosing model (ADM) was designed to interact with our existing CPOE application to provide decision support enabling complex pediatric dose calculations based on chronological age, gestational age, weight, care area in the hospital, indication, and level of renal impairment. Given that weight is a critical component of medication dosing that may change over time, alerting logic was added to guard against erroneous entry or outdated weight information. RESULTS: Pediatric CPOE was deployed in a staggered fashion across 6 care areas over a 14-month period. Safeguards to prevent miskeyed values became important in allowing providers the flexibility to override the ADM logic if desired. Methods to guard against over- and under-dosing were added. The modular nature of our model allows us to easily add new dosing scenarios for specialized populations as the pediatric population and formulary change over time. CONCLUSIONS: The medical needs of pediatric patients vary greatly from those of adults, and the information systems that support those needs require tailored approaches to design and implementation. When a single CPOE system is used for both adults and pediatrics, safeguards such as redirection and suppression must be used to protect children from inappropriate adult medication dosing content. Unlike other pediatric dosing systems, our model provides active dosing assistance and dosing process management, not just static dosing advice.

The DEDUCE Guided Query tool: providing simplified access to clinical data for research and quality improvement.

In many healthcare organizations, comparative effectiveness research and quality improvement (QI) investigations are hampered by a lack of access to data created as a byproduct of patient care. Data collection often hinges upon either manual chart review or ad hoc requests to technical experts who support legacy clinical systems. In order to facilitate this needed capacity for data exploration at our institution (Duke University Health System), we have designed and deployed a robust Web application for cohort identification and data extraction--the Duke Enterprise Data Unified Content Explorer (DEDUCE). DEDUCE is envisioned as a simple, web-based environment that allows investigators access to administrative, financial, and clinical information generated during patient care. By using business intelligence tools to create a view into Duke Medicine's enterprise data warehouse, DEDUCE provides a Guided Query functionality using a wizard-like interface that lets users filter through millions of clinical records, explore aggregate reports, and, export extracts. Researchers and QI specialists can obtain detailed patient- and observation-level extracts without needing to understand structured query language or the underlying database model. Developers designing such tools must devote sufficient training and develop application safeguards to ensure that patient-centered clinical researchers understand when observation-level extracts should be used. This may mitigate the risk of data being misunderstood and consequently used in an improper fashion.

Culture counts--sustainable inpatient computerized surveillance across Duke University Health System.

PURPOSE: The authors report on the managerial and logistical details of deploying a computerized adverse drug event surveillance system that was at first a grant-funded research project and ultimately was changed to a sustained safety-monitoring application serving 3 different hospitals. METHODS: Surveillance was deployed in 3 phases to 2 community-based hospitals and an academic medical center. A logic-based rules engine surveyed electronic records for laboratory, medication, and demographic information indicative of safety concerns. Potential adverse events triggered manual chart review by pharmacists to verify patient harm. RESULTS: During Phase 1, the research team created trigger rules for each hospital. In Phase 2, the trigger review was transitioned to hospital personnel and rule sets were reshaped for specific hospital needs. In Phase 3, surveillance was integrated into daily work flows and organizational balanced scorecards where it was accepted as a quantitative measure of medication safety performance. DISCUSSION AND CONCLUSION: Computerized surveillance helps detect potentially harmful events regardless of hospital size. Active leadership, change-tolerant culture, and hospital pharmacy practice models significantly impact successful adoption. Entrenched cultural issues impeded sustainability at the academic center but not at the 2 community hospitals. Tailoring surveillance to the needs of different inpatient settings is crucial to developing a sustainable model.

Tailoring adverse drug event surveillance to the paediatric inpatient.

INTRODUCTION: Although paediatric patients have an increased risk for adverse drug events, few detection methodologies target this population. To utilise computerised adverse event surveillance, specialised trigger rules are required to accommodate the unique needs of children. The aim was to develop new, tailored rules sustainable for review and robust enough to support aggregate event rate monitoring. METHODS: The authors utilised a voluntary staff incident-reporting system, lab values and physician insight to design trigger rules. During Phase 1, problem areas were identified by reviewing 5 years of paediatric voluntary incident reports. Based on these findings, historical lab electrolyte values were analysed to devise critical value thresholds. This evidence informed Phase 2 rule development. For 3 months, surveillance alerts were evaluated for occurrence of adverse drug events. RESULTS: In Phase 1, replacement preparations and total parenteral nutrition comprised the majority (36.6%) of adverse drug events in 353 paediatric patients. During Phase 2, nine new trigger rules produced 225 alerts in 103 paediatric inpatients. Of these, 14 adverse drug events were found by the paediatric hypoglycaemia rule, but all other electrolyte trigger rules were ineffective. Compared with the adult-focused hypoglycaemia rule, the new, tailored version increased the paediatric event detection rate from 0.43 to 1.51 events per 1000 patient days. CONCLUSIONS: Relying solely on absolute lab values to detect electrolyte-related adverse drug events did not meet our goals. Use of compound rule logic improved detection of hypoglycaemia. More success may be found in designing real-time rules that leverage lab trends and additional clinical information.

Characteristics of ambulatory anticoagulant adverse drug events: a descriptive study.

BACKGROUND: Despite the high frequency with which adverse drug events (ADEs) occur in outpatient settings, detailed information regarding these events remains limited. Anticoagulant drugs are associated with increased safety concerns and are commonly involved in outpatient ADEs. We therefore sought to evaluate ambulatory anticoagulation ADEs and the patient population in which they occurred within the Duke University Health System (Durham, NC, USA). METHODS: A retrospective chart review of ambulatory warfarin-related ADEs was conducted. An automated trigger surveillance system identified eligible events in ambulatory patients admitted with an International Normalized Ratio (INR) >3 and administration of vitamin K. Event and patient characteristics were evaluated, and quality/process improvement strategies for ambulatory anticoagulation management are described. RESULTS: A total of 169 events in 167 patients were identified from December 1, 2006-June 30, 2008 and included in the study. A median supratherapeutic INR of 6.1 was noted, and roughly half of all events (52.1%) were associated with a bleed. Nearly 74% of events resulted in a need for fresh frozen plasma; 64.8% of bleeds were classified as major. A total of 59.2% of events were at least partially responsible for hospital admission. Median patient age was 68 y (range 36-95 y) with 24.9% initiating therapy within 3 months prior to the event. Of events with a prior documented patient visit (n = 157), 73.2% were seen at a Duke clinic or hospital within the previous month. Almost 80% of these patients had anticoagulation therapy addressed, but only 60.0% had a follow-up plan documented in the electronic note. CONCLUSIONS: Ambulatory warfarin-related ADEs have significant patient and healthcare utilization consequences in the form of bleeding events and associated hospital admissions. Recommendations for improvement in anticoagulation management include use of information technology to assist monitoring and follow-up documentation, avoid drug interactions, and engage patients in their care.

Sharing adverse drug event data using business intelligence technology.

INTRODUCTION: Duke University Health System uses computerized adverse drug event surveillance as an integral part of medication safety at 2 community hospitals and an academic medical center. This information must be swiftly communicated to organizational patient safety stakeholders to find opportunities to improve patient care; however, this process is encumbered by highly manual methods of preparing the data. DESCRIPTION OF CASE: Following the examples of other industries, we deployed a business intelligence tool to provide dynamic safety reports on adverse drug events. Once data were migrated into the health system data warehouse, we developed census-adjusted reports with user-driven prompts. Drill down functionality enables navigation from aggregate trends to event details by clicking report graphics. Reports can be accessed by patient safety leadership either through an existing safety reporting portal or the health system performance improvement Web site. DISCUSSION: Elaborate prompt screens allow many varieties of reports to be created quickly by patient safety personnel without consultation with the research analyst. The reduction in research analyst workload because of business intelligence implementation made this individual available to additional patient safety projects thereby leveraging their talents more effectively. CONCLUSIONS: Dedicated liaisons are essential to ensure clear communication between clinical and technical staff throughout the development life cycle. Design and development of the business intelligence model for adverse drug event data must reflect the eccentricities of the operational system, especially as new areas of emphasis evolve. Future usability studies examining the data presentation and access model are needed.

Computerized surveillance of opioid-related adverse drug events in perioperative care: a cross-sectional study.

BACKGROUND: Given the complexity of surgical care, perioperative patients are at high risk of opioid-related adverse drug events. Existing methods of detection, such as trigger tools and manual chart review, are time-intensive which makes sustainability challenging. Using strategic rule design, computerized surveillance may be an efficient, pharmacist-driven model for event detection that leverages existing staff resources. METHODS: Computerized adverse drug event surveillance uses a logic-based rules engine to identify potential adverse drug events or evolving unsafe clinical conditions. We extended an inpatient rule (administration of naloxone) to detect opioid-related oversedation and respiratory depression to perioperative care at a large academic medical center. Our primary endpoint was the adverse drug event rate. For all patients with a naloxone alert, manual chart review was performed by a perioperative clinical pharmacist to assess patient harm. In patients with confirmed oversedation, other patient safety event databases were queried to determine if they could detect duplicate, prior, or subsequent opioid-related events. RESULTS: We identified 419 cases of perioperative naloxone administration. Of these, 101 were given postoperatively and 69 were confirmed as adverse drug events after chart review yielding a rate of 1.89 adverse drug events/1000 surgical encounters across both the inpatient and ambulatory settings. Our ability to detect inpatient opioid adverse drug events increased 22.7% by expanding surveillance into perioperative care. Analysis of historical surveillance data as well as a voluntary reporting database revealed that 11 of our perioperative patients had prior or subsequent harmful oversedation. Nine of these cases received intraoperative naloxone, and 2 had received naloxone in the post-anesthesia care unit. Pharmacist effort was approximately 3 hours per week to evaluate naloxone alerts and confirm adverse drug events. CONCLUSION: A small investment of resources into a pharmacist-driven surveillance model gave great gains in organizational adverse drug event detection. The patients who experienced multiple events are particularly relevant to future studies seeking risk factors for opioid induced respiratory depression. Computerized surveillance is an efficient, impactful, and sustainable model for ongoing capture and analysis of these rare, but potentially serious events.

Probing the functional impact of sequence variation on p53-DNA interactions using a novel microsphere assay for protein-DNA binding with human cell extracts.

The p53 tumor suppressor regulates its target genes through sequence-specific binding to DNA response elements (REs). Although numerous p53 REs are established, the thousands more identified by bioinformatics are not easily subjected to comparative functional evaluation. To examine the relationship between RE sequence variation -- including polymorphisms -- and p53 binding, we have developed a multiplex format microsphere assay of protein-DNA binding (MAPD) for p53 in nuclear extracts. Using MAPD we measured sequence-specific p53 binding of doxorubicin-activated or transiently expressed p53 to REs from established p53 target genes and p53 consensus REs. To assess the sensitivity and scalability of the assay, we tested 16 variants of the p21 target sequence and a 62-multiplex set of single nucleotide (nt) variants of the p53 consensus sequence and found many changes in p53 binding that are not captured by current computational binding models. A group of eight single nucleotide polymorphisms (SNPs) was examined and binding profiles closely matched transactivation capability tested in luciferase constructs. The in vitro binding characteristics of p53 in nuclear extracts recapitulated the cellular in vivo transactivation capabilities for eight well-established human REs measured by luciferase assay. Using a set of 26 bona fide REs, we observed distinct binding patterns characteristic of transiently expressed wild type and mutant p53s. This microsphere assay system utilizes biologically meaningful cell extracts in a multiplexed, quantitative, in vitro format that provides a powerful experimental tool for elucidating the functional impact of sequence polymorphism and protein variation on protein/DNA binding in transcriptional networks.

Reevaluating the safety profile of pediatrics: a comparison of computerized adverse drug event surveillance and voluntary reporting in the pediatric environment.

OBJECTIVES: Children are at exceptionally high risk for adverse drug events. At Duke University Hospital, computerized adverse drug event surveillance and voluntary safety reporting systems work synergistically to identify adverse drug events. Here we identify the most deleterious drug classes to pediatric inpatients and determine which detection methodology provides the greatest opportunity to reduce harm. PATIENTS AND METHODS: We evaluated all of the medication-related events detected by our computerized surveillance and safety reporting systems over a 1-year period for Duke University Hospital pediatric inpatients. Events from both systems were scored for severity and assigned a drug event category. Surveillance events were additionally scored for causality. RESULTS: A total of 849 medication-related reports were entered into the safety reporting system, and 93 caused patient harm, resulting in an adverse drug event rate of 1.8 events per 1000 pediatric patient-days. Seventy eight of the 1537 medication-related events detected by surveillance resulted in patient harm, giving a rate of 1.6 events per 1000 patient-days. The most common events identified by the safety reporting system were failures in the medication use process (26.9%), drug omissions (16.1%), and dose- or rate-related events (12.9%). The most frequent adverse drug event surveillance categories were nephrotoxins (20.7%), narcotics and benzodiazepines (19.3%), and hypoglycemia (11.5%). Most voluntarily reported events originated in ICUs (72.0%), whereas surveillance events were split evenly across intensive and general care. There was little overlap between methodologies. CONCLUSIONS: The epidemiology of pediatric adverse drug events is best addressed by using voluntary reporting in tandem with other strategies, such as computerized surveillance and targeted chart review. Although voluntary reporting excels at identifying administration errors, surveillance excels at detecting adverse drug events caused by high-risk medications and identifies evolving conditions that may provoke imminent patient harm. Surveillance underperformed in pediatrics when compared with adult detection rates, suggesting that tailored rules may be necessary for a robust pediatric adverse drug event surveillance system.

Divergent evolution of human p53 binding sites: cell cycle versus apoptosis.

The p53 tumor suppressor is a sequence-specific pleiotropic transcription factor that coordinates cellular responses to DNA damage and stress, initiating cell-cycle arrest or triggering apoptosis. Although the human p53 binding site sequence (or response element [RE]) is well characterized, some genes have consensus-poor REs that are nevertheless both necessary and sufficient for transactivation by p53. Identification of new functional gene regulatory elements under these conditions is problematic, and evolutionary conservation is often employed. We evaluated the comparative genomics approach for assessing evolutionary conservation of putative binding sites by examining conservation of 83 experimentally validated human p53 REs against mouse, rat, rabbit, and dog genomes and detected pronounced conservation differences among p53 REs and p53-regulated pathways. Bona fide NRF2 (nuclear factor [erythroid-derived 2]-like 2 nuclear factor) and NFkappaB (nuclear factor of kappa light chain gene enhancer in B cells) binding sites, which direct oxidative stress and innate immunity responses, were used as controls, and both exhibited high interspecific conservation. Surprisingly, the average p53 RE was not significantly more conserved than background genomic sequence, and p53 REs in apoptosis genes as a group showed very little conservation. The common bioinformatics practice of filtering RE predictions by 80% rodent sequence identity would not only give a false positive rate of approximately 19%, but miss up to 57% of true p53 REs. Examination of interspecific DNA base substitutions as a function of position in the p53 consensus sequence reveals an unexpected excess of diversity in apoptosis-regulating REs versus cell-cycle controlling REs (rodent comparisons: p < 1.0 e-12). While some p53 REs show relatively high levels of conservation, REs in many genes such as BAX, FAS, PCNA, CASP6, SIVA1, and P53AIP1 show little if any homology to rodent sequences. This difference suggests that among mammalian species, evolutionary conservation differs among p53 REs, with some having ancient ancestry and others of more recent origin. Overall our results reveal divergent evolutionary pressure among the binding targets of p53 and emphasize that comparative genomics methods must be used judiciously and tailored to the evolutionary history of the targeted functional regulatory regions.

Low hanging fruit: a subset of human cSNPs is both highly non-uniform and predictable.

We present a point mutation classification method that contrasts SNP databases and has the potential to illuminate the relative mutational load of genes caused by codon bias. We group point variation gleaned from public databases by their wild-type and mutant codons, e.g. codon mutation classes (CMCs, 576 possible such as ACG-->ATG), whose frequencies in a database are assembled into a BLOSUM-style matrix describing the likelihood of observing all possible single base codon changes as tuned by the intertwined effects of mutation rate and selection. The rankings of the CMCs in any database are reshuffled according to the population stratification of the typical genotyping experiment producing that resource's data. Analysis of four independent databases reveals that a considerable fraction of mutation in functional genes can be described by a few CMCs regardless of gene identity or population stratification in the genotyping experiment. For example, the top 5% (29/576) of CMCs account for 27.4% of the observed variants in dbSNP while the bottom 5% account for only 0.02%. For non-synonymous disease-causing mutation, 40.8% are described by the top 5% of all possible non-silent CMCs (22/438). Overall, the most observed polymorphism is a G-->A transition at CpG dinucleotides causing ACG, TCG, GCG, and CCG to frequently undergo silent mutation in any gene due to the putative lack of impact on the protein product. In order to assess how well CMC spectrums estimate the aggregate non-synonymous mutational trends of a single gene, a CMC matrix was applied to seven unrelated genes to compute the most likely point mutations. In excess of 87% of these mutation predictions are historically known to play an important role in a disease state according to published literature. CMC-based mutation prediction may aid design and execution of direct association genotyping studies.