News Column

"System and Methods for Health Analytics Using Electronic Medical Records" in Patent Application Approval Process

September 8, 2014

By a News Reporter-Staff News Editor at Diabetes Week -- A patent application by the inventor Murata, Glen H. (Albuquerque, NM), filed on February 17, 2014, was made available online on August 28, 2014, according to news reporting originating from Washington, D.C., by NewsRx correspondents (see also Patents).

This patent application has not been assigned to a company or institution.

The following quote was obtained by the news editors from the background information supplied by the inventors: "Problems of poor outcomes, high costs, and declining primary care workforce persist in the national health care system because the health care delivery system is based on an acute care model. Typically, a patient seeks health care only in response to symptoms, placing the onus for initial contact on the patient, often the person with the least knowledge of the condition or illness in question. Planning by the physician is done on a case-by-case basis for only those patients seeking care. Finally, intervention is typically reactive, offered to ameliorate a condition only after it has progressed to the point that the patient is symptomatic, when the opportunity to treat the condition in an earlier, more responsive stage is lost.

"These problems are compounded by reliance upon the outpatient visit as the principal means of delivering medical services. The 'visit-based' approach often precludes services from being received by the neediest patients, i.e., those with access barriers who never present for treatment. Progress is tied to the next available appointment, not to the responsiveness of the disease to treatment. For example, in the case of diabetes management, insulin titrations often occur over several months even though treatment response can be assessed in just a few days.

"As known in the art, dashboard systems are used to present current or contemporary information about a patient, for example, laboratory results. However, these systems are limited in that they do not provide enough information to determine which results are actionable. Dashboards present large volumes of unprocessed data that have to be interpreted out-of-context unless the clinician wants to do a chart review on every case. Chart reviews are rarely built into the workday and may require skills that he or she does not have. After such an effort has been made, it is infuriating to learn that the abnormality has already been treated. The burden of alerts is placed upon the person least able to hand it which impairs the delivery of care to other patients. Finally, the large volume of data makes it even more difficult for the clinicians to prioritize their tasks and tend to patients who need them the most.

"Administrative processes and financial incentives still favor the acute-care, visit-based approach, despite its disadvantages. Examples include the requirement that a 'primary diagnosis' for an outpatient encounter be identified and higher reimbursement for an office visit than an equally effective telephone call.

"Health analytics involves the extensive use of data, statistical and qualitative analysis, explanatory and predictive modeling. Current art in health analytics is based upon administrative databases. One such administrative database is a claims database that includes records consisting of claims for services provided by organizations or individual providers. Unfortunately, there are many problems associated with the use of such data for health analytics. Important categories of clinical data do not result in charges (e.g. vital signs, drug allergies). As a result, claims databases often do not have large domains of data of vital importance to clinicians. More problematic is that claims databases capture what procedures were done but not the results. For example, the data set may contain charges for antibiotic sensitivities for a bacterial isolate but not the results for each antibiotic tested.

"Furthermore, claims data use billing codes and terminology (e.g. CPT-4), while clinical data is organized by medical taxonomies (e.g. SNOMED-CT). Lack of appropriate coding or standardized nomenclature makes the retrieval of information difficult.

"In addition, it is rarely necessary to link certain charges with others, while it is very important to link clinical findings with one another. Claims databases may not meet the requirements of a highly normalized relational data system that allows these relationships to be analyzed.

"Claims are submitted to maximize reimbursement--not to describe the process of care. As a result, there is dissociation between what is billed and what transpired.

"There can be a substantial delay in the capture of claims data because of time required for claims processing, review, and final determination. Claims data is therefore of limited utility for real time decision support.

"In addition, most claims data systems require data transfer agreements between insurance carriers and health care systems. These agreements often require extensive negotiation in areas of legal liability, protection of patient information, authorization/authentication of users, physical security measures, etc.

"Many carriers are unwilling to participate in these arrangements because the risks are not offset by the rewards. Furthermore, patients may change insurance plans frequently and often involuntarily. This problem results in a fragmentation of information across health plans. While the active carrier may provide information about the patient's current status, it may not have enough information to evaluate long term process of care.

"The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2010 was designed to promote the widespread adoption of EMRs so that clinical data would eventually be available for these purposes. There has been a steady increase in the adoption of EMRs in response to financial incentives. However, the definitions for 'meaningful use'--a mandatory requirement--does not include analysis of data across populations. As a result, many commercial products are designed to meet HITECH standards but not maximize outcomes, lower costs, or improve efficiency. Open health care systems are faced with an additional barrier. These systems typically do not have their own pharmacies or laboratories and rely heavily on outside vendors for these services. Important sources of clinical data for a population of interest can thus accrue in separate repositories. Their consolidation into a comprehensive database requires the additional steps of data transfer and integration.

"All accredited health care institutions must have quality assurance programs and undergo periodic audits by outside organizations. Clinicians must be licensed, pass certifying exams, participate in continuing medical education, and be reviewed by their peers on a regular basis. At first glance, it is surprising that these collective efforts have failed. However, current quality improvement processes adopted by institutions have critical deficiencies.

"First, the current processes are limited in scope. At any moment in time, only a small proportion of clinicians and processes are under review. Cases usually come to attention only when there is an egregious outcome that results in a malpractice suit. 'Root cause analysis' then focuses upon a few individuals or the one or two processes that were the immediate cause. The likelihood that most practitioners will benefit from (or even be aware of) the changes is small. Much greater improvement can be achieved by correcting less egregious problems affecting the entire enterprise. Thus, the emphasis should change from managing outliers to improving accepted but suboptimal standards of practice used by everyone else.

"Second, most audits performed by quality improvement services are retrospective--whether the reviews are prompted by an adverse event or randomly selected. This approach guarantees that patients will be exposed to some period of risk. For most facilities, 'risk management' does little to manage risk--that is, to reduce exposures before an adverse event. Instead, the term usually refers to identifying culpable parties, crafting a legal defense, and negotiating financial settlements after such events have occurred. Meaningful risk management should decrease the probability of unfavorable outcomes, shorten the period of exposure, or reduce their impact. This goal can be achieved by evaluating processes every day and across the enterprise; changing those that pose high risk; and more closely observing patients for whom the risk cannot be reduced. For example, 'missed' cancer screening tests should be identified today to reduce the incident of advanced stage malignancies in the future. Patients not taking their medications today should undergo counseling to prevent unfavorable events later. Those with declining renal function should be referred to nephrology today to decrease the likelihood that they will require dialysis. Thus, effective risk management requires regular and real-time retrieval of information about a health system's most critical processes.

"Third, manual reviews of paper charts are resource-intensive, time-consuming, and expensive--if the charts can be found at all. Data are often manually extracted onto paper forms and hand-tabulated. The conclusions are then summarized but may not be disseminated to the clinical staff. The preferred strategy is to use information technology to assemble data that is standardized, coded, and stored in an electronic format. Chart reviews should be reserved only for audits involving non-standardized data not easily retrieved. This approach makes optimal use of the reviewer's skill and time and avoids hand-processing of large amounts of data--a task which humans do poorly. In other words, people should focus on tasks that are 'patient-centric' while computers should handle tasks that are 'data centric'.

"Fourth, current processes provide inadequate sampling. Treatment outcomes can vary greatly because of differences in patient attitudes, motivation, health literacy, behaviors, access to care, cultural beliefs, socio-economic status, and other factors beyond the influence of the most competent clinician. Only a large sample provides a meaningful estimate of performance when there is large variation in the outcome metric. Thus, limited sampling defeats the replication of 'best practices' at the first step--distinguishing good processes from bad ones. Fifth, current processes include meaningless comparisons. Patients differ in many clinical factors that influence the outcome such as genetic predisposition to disease, physiologic traits, severity of illness, co-morbidities, types of treatment, and time on treatment. In standard assessments of quality, almost no attention is paid to clinical determinants of the outcome. As a result, differences between providers may be falsely attributed to the quality of their care when the cause is a difference in the patients they treat. These comparisons become meaningful if there are controls for these covariates--that is, using statistics to eliminate the effect of patient attributes. This process requires retrieving large volumes of material in many domains and multivariate statistical methods. Because most quality improvement programs do not use such a sophisticated approach, the improvement process is again defeated at the first step.

"Lastly, some current processes are rated against standards that may be irrelevant to the population at hand. For example, providers are often rated by their adherence to practice guidelines. Unfortunately, it is often unclear if such guidelines are feasible for or even relevant to their practices. Many recommendations are based upon randomized clinical trials conducted in academic medical centers. These studies involve highly motivated and informed subjects; pay participants for their time and effort; offer the intervention for free; provide immediate access to world experts; follow a fairly rigid protocol; are monitored at all times by highly trained personnel; and last for a short time. Patients who have unfavorable attitudes, behaviors, or mental functioning; cannot afford the time or travel; or have cultural barriers to care do not participate at all. As a result, the findings of the trial may not be relevant when the intervention is used in a different population. The preferred approach is to gather information on the feasibility and impact of the intervention in the entire population served by the health system. The standard should be internal--that is, the best that can be achieved with that intervention given the local population and circumstances.

"These observations suggest that a dramatic improvement in quality, efficiency, and cost can be achieved if large volumes of data can be retrieved on every patient and provider in real-time; data are analyzed in a rigorous manner; and the results used to target the institution's highest priorities at any given time.

"Therefore, there is a need for improved health analytics including analytics that create a new standard for quality improvement and cost containment activities. The invention uses Electronic Medical Records (EMRs) to satisfy this need. Information from EMRs is superior to administrative data in terms of content, quality, relevance and timeliness. Accordingly, analysis of such data produces a more accurate assessment of clinical status than can be derived from claims."

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventor's summary information for this patent application: "According to the invention, data is collected, shared, and analyzed. Data collection can be characterized by the adoption and meaningful use of Electronic Medical Records (EMRs). Data sharing is characterized by the distribution of findings to every part of the organization responsible for the patient's care. Data analysis is characterized by the adoption of enterprise data warehouses and analytic tools.

"Instead of an administrative database such as a claims database, the invention uses information derived from EMRs. The invention is based upon a far more sophisticated approach to health analytics that takes full advantage of a robust data repository built from EMRs. An EMR is a systematic collection of electronic health information about individual patients or populations. An EMR is essentially a digital version of a paper chart in a clinician's office. It contains the medical and treatment history of patients and can be grouped to represent patients in a particular practice.

"An EMR allows a clinician to track data over time, easily identify which patients are due for preventative screenings, check how patients are doing on certain parameters such as blood pressure readings or vaccines and monitor and improve overall quality of care within the practice. EHRs may include a range of data including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, and demographics like age.

"An EMR is said to make the process of patient record-keeping easier, more accurate and comprehensive, and more efficient. Physicians can use a desktop, laptop or electronic clipboard to navigate through patients' charts and record notes. Other types of data can be downloaded into EMRs from outside data sources. The greatest advantage is that all information can be aggregated into a single data source, and multiple users can access that data simultaneously. EMRs minimize the problems of lost charts or missing reports and often have functionalities that improve patient flow and help the clinician make complicated decisions.

"EMRs are capable of being shared across different health care settings. In some cases, this sharing can occur by way of network-connected, enterprise-wide information systems and other information exchanges involving multiple health care institutions.

"The invention derives highly relevant parameters from complex analysis or robust statistical treatment of raw data. These processes reduce the volume of data flowing to practitioners while increasing their utility. In addition, the invention performs causal analysis for poor outcomes which may prevent further patients from exposure to less than optimal processes.

"Rather than external practice guidelines, the invention uses data from specific institutions to create facility-specific standards. This functionality assures that the standards are relevant and feasible for the population of interest.

"In embodiments of the invention in which health analytics are used to develop recommendations, the recommendations are made for the entire patient population, not just individuals. This functionality allows patients to be stratified according to their likelihood of benefit--a complex task in which patients are compared to one another over a variety of critical attributes. This functionality greatly increases an institution's ability to manage its resources.

"Going beyond risk scores to trigger actions--that is, the likelihood that a patient will develop some future event--the invention uses a much broader range of parameters to target patients for treatment intensification including severity, complexity, acuity, actionability, need, and likelihood of benefit.

"The invention creates alerts that define what action should be taken, when it should be taken, and by whom. Alerts are delivered to all levels of the organization--from management to clerical staff--so that the entire institution is involved in improving the quality of its services. The invention supports the patient centered medical home by allowing staff to function at the top of their capabilities and circumvent the bottleneck created by designating a 'provider' as the sole recipient.

"Provider performance is most often compared to expected standards of practice whether they are feasible or relevant to the population in question. The invention provides information of what can be achieved by the institution or much detail about how providers perform relative to one another. The invention compares actual outcomes of providers while adjusting for multiple differences in their panels. This approach is the only one supporting replication of best practices.

"Importantly, the invention applies local decision rules to patients at the point of care so that recommendations are individualized and timely.

"The invention divides the tasks of a physician into those that are data-intensive, others that are protocol-driven, and still others that are patient-centric. Data-intensive tasks are those which require often complex interpretation of large volumes of information including, for example, processing of view alerts, responding to clinical reminders, screening prescriptions for dosing errors and drug interactions, and reviewing laboratory results. Protocol-driven tasks are those in which care is largely dictated by widely accepted standards and for which there is little latitude in decision-making. Examples include cancer screening, vaccinations, and other preventive care; drug titration; timing of subsequent laboratory tests; and special examinations for specific diseases (such as screening for diabetic retinopathy). The third category is comprised of tasks that are patient-centered, cannot be standardized, and require a high level of interpersonal skills. One example is the complex assessment of patient values, education, negotiation, and goal-setting required for the optimal care of type 1 diabetes. Physician skills tend to vary across categories; are least well developed for those that are data-centric; and are best suited for those that are patient-centered. According to the invention, the clinician retains authority over all aspects of the practice. However, its day-to-day operation relies heavily upon data accrued from EMR's in near real-time. Analytical programs are loaded on institutional servers, set to update regularly, and process information on behalf of the clinician.

"Protocol-driven tasks are divided into those handled by the Patient-Aligned Care Team (PACT) and those done at the institutional level. The difference between the two is the level of clinician input. The tasks performed by PACT team include routine re-assessment of disease status, standard titration of medications, and routine monitoring for side-effects and complications. These procedures are well defined for certain diseases but treatment parameters must be set by the clinician. On the other hand, preventive services are so standardized that the clinician needs only to decide that they are appropriate. It seems reasonable for the institution to provide services to which the clinician subscribes.

"The clinician retains responsibility for patient-centric activities. Because many tasks have been off-loaded to other components of the practice, much more attention can be directed to these critical patient-provider interactions. The provider also has the option of setting individual patient parameters for the analytics. Examples include critical values for results reporting, cycle times for periodic assessments, and 'opt-outs' for selected services. When the PACT team is assembled, the clinician defines the scope of practice for its members, designs templated progress notes to document team care, and creates scripted interviews for telephone contacts. The provider can then write standing orders for each patient that govern the operation of the PACT team including drug types, dose titrations, titration intervals, outcome measurements, testing frequency, and stopping rules. Finally, the provider may subscribe to services provided by the institution including vaccinations, cancer screening, and patient education.

"In one embodiment, the programs according to the invention are programmed in Microsoft SQL Server, a mainframe computer program for relational databases. It is contemplated that the programs may be installed on institutional servers tied to a data warehouse, set to update automatically, and is most effective when data is captured in real time.

"In one embodiment, the programs of the invention are installed on servers connected to the institutional data repository. The invention assembles records from a wide variety of data sources. However, unlike registries, the invention examines complex associations, synthesizes new clinical parameters, supports robust statistical analyses of the data, and executes algorithms that replicate the decision process of clinicians. The end-result of this analysis is one to over a dozen main tables written to the server.

"Data may be retrieved into a table in a variety of formats. These tables can be placed directly in a protected folder in the user's space on the institutional computer system thereby allowing queries to be performed on the data such as by a desktop application. The use of tables also facilitates transferring data to a statistical package, merging data with word processing software to generate letters, and converting the data to a tracking log for subsequent interventions.

"The invention assesses and improves quality of health care delivery. Data from EMRs for one or more patients of a patient population is retrieved. Data is retrieved by finding data expressed in non-standardized terms. This is accomplished by searching the EMR for a root syllable, an acronym, a synonym, an abbreviation, or a name variation. Non-numeric symbols from the data are eliminated and any outliers are removed using a mean value calculated for each patient of the patient population.

"The data may be analyzed for a variety of purposes, including to determine variation in performance of a practice site, a group practice, or an individual clinician for the patient population on a given treatment. A comprehensive medication history database may be constructed for patients of the patient population. Provider responses to actionable clinical findings of patients of the patient population are evaluated. In addition, the invention may determine failures of a medical condition of one or more patients, for example, a failure to follow an indicated treatment, an inadequate dose of medication, an inadequate duration of treatment, a delay in switching unsuccessful strategies, or a failure to take a medication. Abnormal screening tests may be used to track progress of patients of the patient population. Abnormal screening tests may be evaluated by rating a referral to specialty care, scheduling an appointment, making a visit, scheduling a biopsy, and processing a specimen. Data from EMRs may also be used to assess use of one or more medications across the patient population. Assessing use of medications may include the steps of identifying polypharmacy patients, monitoring drug adherence, determining out-of-range dosing, deciding dose adjustments, assessing drug interactions, and monitoring a specific drug.

"The EMR data may be analyzed to identify a risk of disease for patients of the patient population. For example, the data may be analyzed by using criteria such as a hospital record, an outpatient encounter, a problem list, a pharmacy record, a laboratory test, a procedure, and a surgical pathology. The EMR data may also be analyzed to prioritize the needs of the patients of the patient population, triage the patients to appropriate members of a health care team, and coordinate repeated cycles of treatment intensification and re-assessment. In addition, EMR data may be used to consider time and expense of travel for treatment by the patients of the patient population.


"FIG. 1 illustrates a block diagram of an analytic platform according to one embodiment of the invention.

"FIG. 2 illustrates a block diagram of programs for analyzing Electronic Medical Record (EMR) data according to one embodiment of the invention.

"FIG. 3 illustrates an exemplary computer system 300 that may be used to implement the programs according to the invention."

URL and more information on this patent application, see: Murata, Glen H. System and Methods for Health Analytics Using Electronic Medical Records. Filed February 17, 2014 and posted August 28, 2014. Patent URL:

Keywords for this news article include: Cancer Vaccines, Electronic Medical Records, Genetics, Information Technology, Information and Data Aggregation, Investment and Finance, Legal Issues, Oncology, Patents, Records as Topic.

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Source: Diabetes Week

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