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Clinical Analytics
- 1. An Overview of Clinical Analytics Michael O. Bice Health Informatics Consultant
- 2. Agenda • Clinical informatics as context for clinical analytics • Uniqueness of medical data mining • Define and describe the practice of clinical analytics • Challenges facing use of clinical analytics • Tools used to analyze clinical data • Use of clinical analytics in different healthcare settings • The future of clinical analytics
- 3. Domains of Clinical Informatics
- 4. Uniqueness of Medical Data Mining • Heterogeneity of medical data ▫ Raw medical data are voluminous and heterogeneous ▫ Medical data may be collected from various images, interviews with the patient, laboratory data, and the physician’s observations and interpretations ▫ All these components may bear upon the diagnosis, prognosis, and treatment of the patient, and cannot be ignored • Ethical, legal, and social issues ▫ Privacy and security considerations ▫ Fear of lawsuits ▫ Need to balance the expected benefits of research against any inconvenience or possible injury to the patient
- 5. Uniqueness of Medical Data Mining • Statistical philosophy: Methods of medical data mining must address ▫ The heterogeneity of data sources ▫ Data structures ▫ The pervasiveness of missing values • Special status of medicine ▫ Outcomes of medical care are life-or-death ▫ They apply to everybody ▫ Medicine is a necessity, not merely an optional luxury, pleasure, or convenience
- 6. Definition of Clinical Analytics (CA) • Clinical analytics encompasses the capture and use of discrete clinical data to identify and measure quality, patient safety, or service line efficiencies and improvements
- 7. Promise of Clinical Analytics • Through careful implementation of health analytics, hospitals can transform unwieldy amalgamations of data into information that can: ▫ Improve patient outcomes ▫ Increase safety ▫ Enhance operational efficiency ▫ Support public health efforts
- 8. Promise of Clinical Analytics • CA applications are designed to place: ▫ timely ▫ relevant ▫ actionable information • Into the hands of all users with a legitimate interest in it
- 9. Current Use of CA • Collecting and leveraging clinical and claims data to enhance patient care cost, safety and efficiency • Data is looked at on a variety of levels ▫ A specific patient ▫ Population-based, such as data specific to a particular physician or to a certain condition, such as diabetes or hypertension • Using rule sets from a wide variety of organizations ▫ Voluntary programs (the Leapfrog Group) ▫ Government sources (the Hospital Compare Database) ▫ Trade organizations (the Council of Teaching Hospitals or the Society for Thoracic Surgeons)
- 10. Current Use of CA • Much of the information that healthcare organizations ultimately choose to report is driven in one of three ways: ▫ Data that they are required to track by the government or other external organizations ▫ Data that healthcare organizations choose to look at that is driven by QA or cost containment opportunities ▫ Information that is required for recertification of professional staff
- 11. Types of CA Practitioners • Pharmacists with formal informatics training (e.g., Masters or Doctorate in Medical Informatics or Informatics fellowship) or extensive clinical informatics experience to develop and maintain pharmacy content • Physicians with informatics experience to translate clinical guidelines and study protocols into CDS interventions • Doctoral-level medical informaticians
- 12. Types of CA Practitioners • Registered nurses (RNs) with informatics training and experience • Dedicated software developers and project managers without a clinical background • Master’s in Health Informatics • Master’s in Health Administration with Concentration in Health Informatics
- 13. CA Continuum • Data extraction tools (Bottom of Hierarchy) ▫ Collect data from existing databases • Data warehouses and data marts • Formatting tools and techniques ▫ Used to "cleanse" the data and convert it to formats that can easily be understood
- 14. CA Continuum • Enterprise reporting and analytical tools ▫ Online analytic process (OLAP) engines and analytical application development tools are for professionals who analyze data and perform business forecasting, modeling and trend analysis • Human intelligence tools (Top of Hierarchy) ▫ Human expertise, opinions and observations
- 15. CA Challenges • Modern medicine generates, almost daily, huge amounts of heterogeneous data. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. • In industry, data are typically viewed as a critical enterprise asset; medicine, in contrast, tends to view data as a byproduct of operations • Clinical analytics continues to be used primarily for retrospective analyses, rather than real-time clinical decision support.
- 16. CA Challenges • Organizational, not data or financial concerns, are holding back adoption. Primary obstacles to widespread analytics adoption include: ▫ Knowing how to use analytics to improve the business ▫ Management bandwidth due to competing priorities ▫ Lack of skills internally ▫ Ability to get the data ▫ Existing culture discourages info sharing
- 17. CA Challenges • Lack of use of tools to support the work of clinical analytics • Lack of money to hire additional appropriately trained clinical informaticians • Rapidly expanding regulatory reporting and compliance requirements along with increasing emphasis on quality measures • Healthcare provider organizations are struggling to understand how the government’s role in clinical analytics is going to evolve in the future
- 18. Inventory of Tools and Best Practices • A multidisciplinary team responsible for creating and maintaining the clinical content • An external repository of clinical content with web-based viewer that allows anyone to review it • An online, interactive, Internet-based tool to facilitate content development and collaboration
- 19. Inventory of Tools and Best Practices • An enterprise-wide tool to maintain the controlled clinical terminology concepts ▫ The availability of a robust, controlled clinical terminology(e.g., SNOMED for problems, LOINC for lab results and ICD-10 for billing diagnoses, etc.) ▫ Many controlled terminologies include some sort of semantic network that maintains various types of relationships among the clinical terms
- 20. Inventory of Tools and Best Practices • Niche vendors that specialize in the development of data warehouses or data mining to assist in this type of analysis (See www. Explorys.com) • Use of data warehouses for clinical purposes is evolving ▫ According to data from the HIMSS Analytics™ Database, approximately 30% of U.S. hospitals presently use a clinical data warehouse ▫ Usage is more widespread among hospital systems and academic health centers
- 21. Case Studies (Duke UHS) • Leveraging enterprise data through computerized patient safety initiatives ▫ Integrated data warehouse ▫ Web-based safety dashboard ▫ Proactive detection and subsequent amelioration of Clostridium difficile colitis rates Prevented 158 potential cases of nosocomially acquired C difficile colitis per year Financial burden of C difficile colitis to range from $3669 to $7234 in additional hospital costs per infected patient, which by conservative analysis translates into a total savings of $578,968
- 22. Case Studies (Duke UHS) • Improving the business cycle: the Duke intensive care nursery ▫ Current and projected losses in the ICNursery ▫ Traditional cost-cutting not feasible ▫ Analysis suggested 4 areas for targeted improvement: MD documentation, medical record coding, revenue modeling, and 3rd party payments ▫ Current and retroactive profits recorded
- 23. Case Studies (Duke UHS) • Leveraging health analytics for emerging health issues ▫ Used its data warehouse to provide a highly refined estimate of patients likely to need H1N1 vaccine (Swine Flu) Inpatient status Diagnosis of chronic disease High–risk mothers and children ▫ Timely and accurate information to the state and to better define DUHS strategy for vaccine administration
- 24. Case Studies (Beth Israel Deaconess Medical Center) • Challenge ▫ Need for a CDS tool capable of identifying the most appropriate imaging test for a specific patient BIDPO physicians had the capability to select from 2,000 orderable radiological studies, many of which were state of the art technologies The abundance of such options also resulted in potentially inappropriate testing, false positives, and potential risk to patients (e.g., contrast injections, interventional procedures, and radiation exposure)
- 25. Case Studies (Beth Israel Deaconess Medical Center) • Solution ▫ An advanced CDS system with computerized provider order entry (CPOE) and real-time insurer authorization ▫ Create a natural language ordering vocabulary ▫ A web-based, physician-designed user interface for Anvita Health’s imaging implementation was then seamlessly integrated into BIDMC’s existing EMR
- 26. Case Studies (Beth Israel Deaconess Medical Center) • Results ▫ CDS positively influenced up to 35% of all ordering decisions, and up to 10% of high-tech radiology decisions were changed ▫ CDS decreased inappropriate imaging, which reduced overall cost trends for the hospital, patients, and the health plan while increasing quality ▫ CDS identified testing contraindications (e.g., contrast dye use) in patients at high risk for adverse reactions
- 27. Future Considerations • Early stages of an information revolution in healthcare, as genomics, pharmacogenomics, and point-of-care decision support converge in a new era of personalized medicine • Active investment in health analytics, data integration, and data sharing are critical to creating efficiencies (Improve Signal to Noise ratio) • New approaches to data visualization and analysis are neededhttp://visual.ly/learn/data-visualization-tools
- 28. Future Considerations CA is the next wave of HIT–converting paper to electronic impulses is a massive undertaking, but clearly not sufficient A widening gap between data collection and data comprehension – An industry that is drowning in data and starving for information and knowledge Conventional cost cutting may have run its course-need for finely grained systems analysis (Duke UHS example)
- 29. Future Considerations Moving CA from few large academic medical centers to general healthcare field (Few players dominate the CA discussions) Distinction between having information and knowledge – and acting on it, either individually or collectively Evolving discipline with significant upside potential
- 30. Pause and Reflect • Why study CA and why now? • What roles should the CEO, CFO and CIO play in bringing CA expertise into the organization? • How would you go about establishing CA capability in your hospital? • What skills and experience will you need to be CA “job ready” upon graduation?
Notas del editor
- As you examine the literature, you find a mixture of terms used to describe “Clinical Analytics.” There are references to Business Intelligence (BI), Health Intelligence, Clinical Knowledge Management (CKM), Healthcare or Medical Data Mining, and Health Analytics. Suspect it is a function of a discipline that is young and evolving. We’ll stay with the use of Clinical Analytics (CA) throughout the presentation. April 6, 2011 Revision: August 12, 2014
- Source: AMIA Board White Paper Core Content for the Subspecialty of Clinical Informatics, JAMIA (2009) Clinical informatics encompasses three spheres of activity: 1. Clinical care (i.e., the provision of clinical services to an individual patient), 2. The health system (i.e., the structures, processes, and incentives that shape the clinical care environment; this includes major health domains such as public health, population health, personal health, health professional education, and clinical research, in addition to clinical care), 3. Information and communications technology (i.e., the tools that enable the efficient capture, delivery, transmission, and use of data, information, and knowledge and the knowledge of how to apply those tools effectively).
- K.J. Cios and G. W. Moore, Uniqueness of medical data mining, Artificial Intelligence in Medicine 26 (2002) 1–24 The major points of uniqueness of medical data may be organized under four general headings: Heterogeneity: Raw medical data are voluminous and heterogeneous. Medical data may be collected from various images, interviews with the patient, laboratory data, and the physician’s observations and interpretations. All these components may bear upon the diagnosis, prognosis, and treatment of the patient, and cannot be ignored. Ethical: The ethical, legal, and social limitations on medical data mining relate to privacy and security considerations, fear of lawsuits, and the need to balance the expected benefits of research against any inconvenience or possible injury to the patient.
- K.J. Cios and G. W. Moore, Uniqueness of medical data mining, Artificial Intelligence in Medicine 26 (2002) 1–24 Statistical: Methods of medical data mining must address the heterogeneity of data sources, data structures, and the pervasiveness of missing values. In any large database, we encounter a problem of missing values. A missing value may have been accidentally not entered, or purposely not obtained for technical, economic, or ethical reasons. Example: AMIA 10x10 Course Lesson 3.1 Slide 11 Missing clinical information during primary care visits (Smith, 2005) Finding-Information reported missing in 13.6% of clinical visits. Status: Finally, medicine has a special status in science, philosophy, and daily life. The outcomes of medical care are life-or-death, and they apply to everybody. Medicine is a necessity, not merely an optional luxury, pleasure, or convenience.
- Source: Clinical Analytics: Can Organizations Maximize Clinical Data? HIMSS Analytics (2010)
- Source: Jeffrey M Ferranti, et al, Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness J Am Med Inform Assoc 2010;17:136-143. These applications, commonly known as business intelligence (BI), place timely, relevant, and actionable information into the hands of all users with a legitimate interest in it.
- Source: Jeffrey M Ferranti, et al, Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness J Am Med Inform Assoc 2010;17:136-143. These applications, commonly known as business intelligence (BI), place timely, relevant, and actionable information into the hands of all users with a legitimate interest in it.
- Source: Clinical Analytics: Can Organizations Maximize Clinical Data? HIMSS Analytics (2010)
- Source: Clinical Analytics: Can Organizations Maximize Clinical Data? HIMSS Analytics (2010) One example provided in this area is the Ongoing Professional Practice Evaluation (OPPE), which examines performance data for all practitioners with privileges on an on-going basis relative to their two-year reappointment process.
- Dean F. Sittig, et al The state of the art in clinical knowledge management: An inventory of tools and techniques IJMI 79 (2010) 44–57 What types of people do you have to help manage your clinical knowledge? All of the organizations studied had one or more of the following types of people involved in the CKM process:
- Source: Defining the Landscape: Data Warehouse and Mining Intelligence Continuum Copyright 2007 by the Healthcare Information and Management Systems Society. At the bottom of the BI hierarchy are extraction and formatting tools which are also known as data-extraction tools. These tools collect data from existing databases for inclusion in data warehouses and data marts. Thus the next level of the BI hierarchy is known as warehouses and marts. Because the data come from so many different, often incompatible systems in various file formats, the next step in the BI hierarchy is formatting tools. These tools and techniques are used to "cleanse" the data and convert it to formats that can easily be understood in the data warehouse or data mart.
- Next, tools are needed to support the reporting and analytical techniques. These are known as enterprise reporting and analytical tools. Online analytic process (OLAP) engines and analytical application development tools are for professionals who analyze data and perform business forecasting, modeling and trend analysis. Human intelligence tools form the next level in the hierarchy and involve human expertise, opinions and observations to be recorded to create a knowledge repository. These tools are at the very top of the BI hierarchy and serve to amalgamate analytical and BI capabilities along with human expertise.
- 1. Modern medicine generates, almost daily, huge amounts of heterogeneous data. For example, medical data may contain CT scan, signals like ECG, clinical information like temperature, cholesterol levels, etc., as well as the physician's interpretation. Those who deal with such data understand that there is a widening gap between data collection and data comprehension. Computerized techniques are needed to help humans address this problem. Source: Cios, Krzysztof J. (Ed.) Medical Data Mining and Knowledge Discovery (2001) http://www.springer.com/public+health/book/978-3-7908-1340-1 2. This latter view is not without promise, but in general this ‘byproduct’ is not being fully leveraged. Source: Jeffrey M Ferranti, et al, Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness J Am Med Inform Assoc 2010;17:136-143. Clinical Analytics in the World of Meaningful Use (February 2011) HIMSS Analytics™ White Paper P. 6
- Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. © MIT 2010 Existing culture discourages info sharing = silos by department, division, discipline, shift, et al
- Dean F. Sittig, et al The state of the art in clinical knowledge management: An inventory of tools and techniques IJMI 79 (2010) 44–57 Further, the rapidly expanding regulatory reporting and compliance requirements along with increasing emphasis on quality measures (e.g., The Joint Commissions’ CORE Measures [27] and National Patient Safety Goals [28] or Medicare’s Physician Quality Reporting Initiative (PQRI) [29], to name just a few… Point #4 Source: Clinical Analytics: Can Organizations Maximize Clinical Data? HIMSS Analytics (2010)
- Dean F. Sittig, et al The state of the art in clinical knowledge management: An inventory of tools and techniques IJMI 79 (2010) 44–57 We identified a variety of tools and current practices for CKM. After reviewing all of the responses to our survey and discussing the summarization of the data, we have identified the following tools and practices as the most widely used in organizations with successful CPOE and CDS implementations Finding: All organizations had such a team and all agreed that these individuals were the most essential component of their CDS success. In addition, they stated that a transparent governance structure for all content-related decision making was also important.
- Dean F. Sittig, et al The state of the art in clinical knowledge management: An inventory of tools and techniques IJMI 79 (2010) 44–57 SNOMED CT Systematized Nomenclature of Medicine -- Clinical Terms Logical Observation Identifiers Names and Codes The purpose of LOINC® is to facilitate the exchange and pooling of clinical results for clinical care, outcomes management, and research by providing a set of universal codes and names to identify laboratory and other clinical observations. ICD = International Classification of Diseases ICD 9 to ICD 10 The deadline for ICD 10 compliance is October 1, 2014 (See CMS post)
- Source: Clinical Analytics: Can Organizations Maximize Clinical Data? HIMSS Analytics (2010) More specifically, approximately 40 percent of hospitals with more than 500 beds use this technology, compared to 18 percent of hospitals with 100 beds or fewer. HIMSSanalytics™ Database (www.himssanalytics.org) January 2011 Clinical Analytics in the World of Meaningful Use (February 2011) HIMSSanalytics™ White Paper P. 5
- Source: Jeffrey M Ferranti, et al, Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness J Am Med Inform Assoc 2010;17:136-143. We also present three case studies that illustrate the use of health analytics to leverage preexisting data resources to support improvements in patient safety and quality of care, to increase the accuracy of billing and collection, and support emerging health issues. More importantly, our dynamic BI interface allows clinicians and leaders to analyze data at the level of the health system as a whole or in a service-specific manner. Using our Web-based safety dashboard, clinicians can identify cohorts of interest, display census-corrected aggregate safety statistics, and click on bars within graphs to ‘drill down’ into encounter-specific or event-specific details . Of greater significance, however, are the rising estimates of serious complications of C difficile infection (ie, surgery, prolonged hospitalization, intensive care services) and the case fatality rate of about 2.2%,40 meaning that the ability to forestall such infections has serious implications for patient wellbeing.
- Source: Jeffrey M Ferranti, et al, Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness J Am Med Inform Assoc 2010;17:136-143. As is the case at many institutions, the DUHS data warehouse was originally designed as a financial system and has only recently been used to support safety, research, and QI. Traditional cost-cutting strategies were not feasible, because nearly 75% of costs were due to personnel, leaving only 25% of costs amenable to reduction through reduced resource utilization.
- Source: Jeffrey M Ferranti, et al, Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness J Am Med Inform Assoc 2010;17:136-143. In the wake of a declaration of a pandemic of H1N1 influenza (‘swine flu’) by the WHO in the spring of 2009,45 the DUHS faced the problem of estimating the amount of vaccine it would need to request in order to meet the needs of the communities it serves. In doing so, the DUHS sought to avoid two undesirable outcomes: (1) ordering too little vaccine, resulting in local shortages and diminished protection among unvaccinated patients and customers served by Duke, or (2) ordering too much vaccine, resulting in waste and potentially contributing to shortages in other locations, as well as incrementally adding to stress on the national vaccine distribution network.
- Source: http://www.anvitahealth.com/pdf/Case%20Study_Anvita%20Health%20Partners%20with%20Beth%20Israel%20Deaconess%20Medical%20Center.pdf
- Source: http://www.anvitahealth.com/pdf/Case%20Study_Anvita%20Health%20Partners%20with%20Beth%20Israel%20Deaconess%20Medical%20Center.pdf © 2009 Anvita, Inc. Controlled natural languages (CNLs) are subsets of natural languages, obtained by restricting the grammar and vocabulary in order to reduce or eliminate ambiguity or complexity. Traditionally, controlled languages fall into two major types: those that improve readability for human readers (e.g. non-native speakers), and those that enable reliable automatic semantic analysis of the language. Source: http://en.wikipedia.org/wiki/Controlled_natural_language (Accessed 8.12.14)
- Source: http://www.anvitahealth.com/pdf/Case%20Study_Anvita%20Health%20Partners%20with%20Beth%20Israel%20Deaconess%20Medical%20Center.pdf Richard Parker, M.D., Medical Director of BIDPO “Anvita Health is providing a breakthrough CDS solution to help confront a major challenge facing physicians today – the abundance of a bewildering number of high tech imaging choices, some of which are extremely powerful, and others which are expensive and risky, yet may not yield a rapid diagnosis.”
- Source: Jeffrey M Ferranti, et al, Bridging the gap: leveraging business intelligence tools in support of patient safety and financial effectiveness J Am Med Inform Assoc 2010;17:136-143. We are in the early stages of a revolution in healthcare, as genomics, proteomics, pharmacogenomics, and point-of-care decision support converge in a new era of personalized medicine. Without timely and appropriate investment in data infrastructure, however, the potential benefits of this revolution may be impeded BI tools allow us to continuously monitor health system performance, separate signals from noise, and scientifically evaluate the return on investment provided by QI initiatives. As patient populations grow and operating budgets are increasingly constrained, such efficiencies will be vital to the success of healthcare institutions.
- Dave Fiser’s recent comment-Need CA competent people (Per Becca Meehan)