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Patient Care Information Systems Suzanne Bakken, RN, DNSc October 23, 2001.

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Presentation on theme: "Patient Care Information Systems Suzanne Bakken, RN, DNSc October 23, 2001."— Presentation transcript:

1 Patient Care Information Systems Suzanne Bakken, RN, DNSc October 23, 2001

2 Purpose To illustrate the manner in which information systems support the patient care process through the examples of computer-based patient records, other patient care systems and patient classification systems

3 Core Phenomena of Informatics Data Information Knowledge –...of relevance to “x”, e.g., patient care

4 What kinds of data, information, and knowledge do we require for patient care?

5 Patient-Specific Data Demographics (age, gender, race, ethnicity, source of admission) Problems (diagnoses, symptoms, reasons for health care encounter) Severity of illness score (APACHE, Medis Groups, Nursing Severity Index) Interventions (risk assessments, procedures, medical interventions, nursing interventions, laboratory tests) Nursing care intensity Outcomes (mortality, morbidity, health services utilization, functional status, quality of life)

6 Agency-Specific Data Medicare case mix Occupancy Hours per patient day Skill mix Utilization patterns Locally developed guidelines, standards of care, critical paths Raw and risk-adjusted outcomes Provider satisfaction Patient satisfaction

7 Domain Information and Knowledge Synthesized evidence –Bibliographic databases –Decision support systems –Standards of care, practice guidelines –Comparative databases

8 “Patient Care” Information Systems Computer-based patient record Ancillary (radiology, pharmacy, laboratory, dietary) - tied to CPR through order entry and results review Patient classification Patient monitoring

9 Example 1: Computer-based Patient Record

10 Definitions IOM Recommendations Attributes Barriers Status

11 Computer-based Patient Record A primary patient record is used by healthcare professionals while providing patient care services to review patient data or document their own observations, actions, or instructions A secondary patient record is derived from the primary record and contains selected data elements to aid nonclinical users (ie., persons not involved in direct patient care) in supporting, evaluating, or advancing patient care

12 Computer-based Patient Record A CPR is an electronic patient record that resides in a system specifically designed to support users by providing accessibility to complete and accurate data, alerts, reminders, clinical decision support systems, links to medical knowledge, and other aids

13 IOM Recommendations Health care professionals and organizations should adopt the CPR as the standard for medical and all other records related to patient care To accomplish this the public and private sectors should join in establishing a CPR Institute (CPRI) to promote and facilitate development, implementation, and dissemination of the CPR

14 IOM Recommendations Both the public and private sectors should expand support for the CPR and CPR system implementation through research, development, and demonstration projects The CPRI should promulgate uniform national standards for data and security to facilitate the implementation of the CPR and its secondary data bases

15 IOM Recommendations The CPRI should review federal and state laws and regulations for the purpose of proposing and promulgating model legislation and regulations to facilitate the implementation and dissemination of the CPR and its secondary data bases and to streamline the CPR and CPR systems

16 IOM Recommendations The costs of CPR system should be shared by those who benefit from the value of the CPR Full costs of implementing and operating CPRs and CPR systems should be factored into reimbursement levels or payment schedules of both public and private sector third-party payers Users of secondary databases should support the costs of creating such data bases

17 IOM Recommendations Heathcare professional schools and organizations should enhance educational programs for students and practitioners in the use of computers, CPRs, and CPR systems for patient care, education, and research

18 Attributes of CPRs and CPR Systems The CPR contains a problem list that clearly delineates the patient’s clinical problems and the current status of each (e.g., the primary illness is worsening, stable, or improving) The CPR encourages and supports the systematic measurement and recording of the patient’s health status and functional level to promote more precise and routine assessment of the outcomes of patient care

19 Attributes of CPRs and CPR Systems The CPR state the logical basis for all diagnoses or conclusions as a means of documenting the clinical rationale for decisions about the management of the patient’s care. This documentation should enhance use of a scientific approach in clinical practice and assist the evolution of a firmer foundation for clinical knowledge

20 Attributes of CPRs and CPR Systems The CPR system addresses patient data confidentiality comprehensively--in particular ensuring that the CPR is accessible only to authorized individuals The CPR is accessible for use in a timely way at any and all times by authorized individuals involved in direct patient care. Simultaneous and remote access to the CPR is possible

21 Attributes of CPRs and CPR Systems The CPR system allows selective retrieval and formatting of information by users. It can provide custom-tailored “views” of the same information The CPR can be linked to both local and remote knowledge, literature, bibliographic, or administrative databases and systems (including those containing clinical practice guidelines or clinical decision support capabilities) so that such information is readily available to assist practitioners in decision making

22 Attributes of CPRs and CPR Systems The CPR can assist, and in some instances, guide the process of clinical problem solving by providing clinicians with decision analysis tools, clinical reminders, prognostic risk assessment, and other clinical aids The CPR supports structured data collection and stores information using a defined vocabulary. It adequately supports direct data entry by practitioners

23 Attributes of CPRs and CPR Systems The CPR can help individual practitioners and healthcare provider institutions manage and evaluate the quality and costs of care The CPR is sufficiently flexible and expandable to support not only today’s basic information needs but also the evolving needs of each clinical specialty and subspecialty

24 Attributes of CPRs and CPR Systems The CPR can be linked with other clinical records of a patient--from various settings and time periods--to provide a longitudinal (lifelong) record of events that may have influenced a person’s health

25 Technological Building Blocks Data exchange and vocabulary standards Systems communications and network infrastructure System reliability and security Linkages to secondary databases

26 Technological Building Blocks Databases and database management systems Workstations Data acquisition and retrieval Text processing Image processing and storage

27 Technological Barriers Human interface and system performance Text processing Confidentiality and security Health data-exchange standards

28 Nontechnological Barriers Unpredictable user behavior Lack of leadership for resolving CPR issues Lack of training for developers Lack of consensus on the content of the CPR Development costs Disaggregated health care environment

29 Status of Issues Related to CPRs CPRI formed Healthcare Information Portability and Accountability Act (K2) IOM report on confidentiality and security Health data-exchange standards

30 Status of Attributes Problem list and status Functional status Logical basis of diagnosis and clinical rationale for treatment Accessibility Confidentiality Linkages to local and remote sources of knowledge Tailored views Structured data entry by providers Storage in standardized vocabulary Decision analysis and decision support aids Manage cost and quality Longitudinal record














44 Example 2: Patient Classification

45 Patient dependency: predict nursing care requirements (aka acuity, nursing care intensity, workload) Severity of illness: predict mortality Diagnosis-related groups: predict resource use, e.g., cost of care, length of stay

46 Essential Elements of Patient Classification Tool for Nursing Care Requirements Tool to predict nursing care requirements for individual patients Sound method of validating the amount of care given to each category of type of patient on each unit and shift Sound method of evaluating the patterns of care delivery by each unit, shift, and staff level Mechanism of revalidating the amount of care by patient category and patterns of care delivery on a periodic basis Method of relating nursing care requirements to staff resource allocation on a shift-by-shift and unit-by-unit basis Method of monitoring the reliability of patient classification over time (deGroot, 1989 )

47 Examples Medicus ~ 37 indicators (e.g., unconscious, bath with assistance, invasive monitoring, patient education) THC = RV X TH THC = total hours of care per patient per day RV = relative value per level of care (standard) TH = target hours per unit of workload (institution or unit- specific) Type I = 0-3 hours/24 and Type V = 18-24 hours/24

48 Examples GRASP Variable # indicators, typically >45 (e.g., emotional status, complexity of clinical judgments, activities of daily living Indicators are unit-specific Time standards are unit-specific

49 Severity of Illness Measures Focus on clinical stability and probability of death Uses Provision of care Determination of potentially ineffective care Risk-adjustment of outcomes for benchmarking

50 Adjusting Risk Var(O) = Var (V) + Var (SE) + Var (RE) Var (O) is the observed variability in patient outcomes across providers Var(V) is the part of Var(O) “validly” attributable to quality of care differences among providers Var(SE) is the systematic error related to differences in patient-specific characteristics among providers Var(RE) is the random error related to residual variability caused by unknown or unmeasured factors.

51 Dimensions of Risk Age Gender Acute clinical stability Principal diagnosis Severity of principal diagnosis Extent and severity of comorbidities Physical functional status Psychological, cognitive, and psychosocial functioning Cultural, ethnic, and socioeconomic attributes and behaviors Health status and quality of life Patient attitudes and preferences for outcomes

52 An Example: Comparing Risk Adjustment Strategies in HIV/AIDS Quality of nursing care of persons with AIDS – link interventions with outcomes Predictors –Apache Acute Physiology scores (APS) –Nursing Severity Index scores (NSI) –Quality Audit Marker (QAM) Outcomes –Mortality –Length of stay

53 APACHE System Acute Physiologic Score (APS) - primarily vital signs and laboratory values (e.g., BUN) –Range of possible scores on 14/17 variables = 0- 211 –No data on creatinine, urine output, or bilirubin Chronic Health Evaluation - presence or absence of specific medical diagnoses Comparative database for benchmarking

54 Nursing Severity Index 34 items (range of score = 0-34) Significant predictor of mortality and length of stay Nursing diagnosis-based (28/34 = NANDA) –Nutrition and metabolism (n=7) –Urinary and fecal elimination (n=5) –Activity and exercise (n=8) –Underlying management issues (n=5) –Psychosocial issues (n=9)

55 Quality Audit Marker Developed to measure functional status in HIV/AIDS QAM ambulation – 2 items QAM self-care – 6 items

56 Descriptive Statistics PredictorsMean(SD) Apache APS34.4(5.9) Nursing Severity Index3.1(1.8) QAM ambulation7.0(1.5) QAM self-care19.6(4.9)

57 Significant predictors Apache APS –Significant predictor of mortality –During hospitalization –3 month –6 month Nursing Severity Index –Did not significantly predict mortality or length of stay QAM self-care –Did not significantly predict mortality or length of stay QAM ambulation –Significant predictor of length of stay –Significant predictor of mortality during hospitalization Model with 4 predictors better than only Apache APS at all points in time

58 Discussion and Questions

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