Evaluation of an automated inferencing engine generating ICD-9CM codes for physician and hospital billing.

Recent Medicare changes to Severity Diagnosis Related Groups (MS-DRGs) for inpatients have made the appropriate and timely coding of services provided by hospitals and physicians a challenge, and require education for clinicians and coders. Clinical departments have limited funds to hire dedicated personnel to code and prepare payor submissions. Automating the process can assist in accurate data collection and reimbursement.

The tyranny of the diagnosis code.

The electronic medical record (EMR), while having acknowledged advantages over the paper record and powerful constituencies advocating its adoption, is not in widespread use. One significant obstacle to its acceptance by physicians has not been addressed--its failure to provide easy input for the patients exact diagnoses and for the retrieval of those diagnoses during subsequent patient care. Furthermore, our system designers have failed to respond to the expansion of the use of the medical record from its origin as simply the physician's memory and communication tool to becoming the building block for our Medical Record Health Information System (MRHIS), where it also supplies the justification for payment for care and is the source of fundamental statistics about health and healthcare. These problems reflect a basic flaw in the application of available information technology to EMR design and data management: We use output codes--the category codes from ICD-9-CM--for input of diagnoses. This fact imposes the tyranny. Our medical record must have these ICD-9-CM codes for the reimbursement system. But, to be accepted as the basic record for medical care, and at the same time, to be truly useful for case retrieval and statistics, medical informatics experts recognize that our EMR must have codes for the exact diagnoses of the patient (diagnosis entities). But no practical method for their input and management has been offered This paper proposes a way to provide easy input of diagnosis entities, and their permanent coding as a workable solution to the problem.

Coding & billing. Will codes turn 10?

All of health care continues to hold its collective breath waiting for the Health and Human Services Department to give its final blessing to a new and improved coding system, ICD-10. But that wait could go on for a few more years.

Detecting possible vaccination reactions in clinical notes.

The Vaccine Safety Datalink is a collaboration between the CDC and eight large HMO's to investigate adverse events following immunization through analysis of medical care databases and patients' medical charts. We modified an existing system called MediClass that uses natural language processing (NLP) and knowledge-based methods to classify clinical encounters recorded in electronic medical records (EMRs). We developed the knowledge necessary for MediClass to detect possible vaccine reactions in the outpatient, ED, and telephone encounters recorded in the EMR of a large HMO. We first trained the system using a manually coded gold standard training set, and achieved high sensitivity and specificity. We then ran a large set of post-immunization encounter records through MediClass to see if our method would generalize. Compared to methods that use administrative and clinical codes assigned to the EMR by clinicians, the system significantly improves the positive predictive value for detecting possible vaccine reactions.

A clinical care costing method.

The Clinical Care Costing Method (CCCM for the Home Health Care Classification (HHCC) System provides an innovative way to determine the cost of clinical nursing practice. This costing methodology consists of three major nursing indicators--Care Components, Actions and Outcomes. These three indicators require Clinical Care Pathways (CCP) to document, track, and code clinical care using the HHCC System. The clinical care costs and/or resources are derived from the time and frequencies of the Action Types for the specific nursing interventions performed by the different type of health care providers to achieve the Outcomes and resolve the Care Component that are used to classify nursing diagnoses/patient problems. This method can also be used to deter-mine the reimbursement for nursing care services retrospectively and once validated prospectively. The Clinical Pathway data provide the evidence that the nursing interventions achieve the desired outcomes.

Incorporating the International Classification of Functioning, Disability, and Health (ICF) into an electronic health record to create indicators of function: proof of concept using the SF-12.

OBJECTIVE: The purpose of this proof-of-concept study was to assess the feasibility of using a generic health measure to create coded functional status indicators and compare the characterization of a stroke population using coded functional indicators and using health-related quality-of-life summary measures alone. DESIGN: Multiple raters assigned International Classification of Functioning, Disability, and Health (ICF) codes to the items of the 12-Item Short Form Health Survey (SF-12). Data for comparing the information from the SF-12 and from ICF codes were derived from the Montreal Stroke Cohort Study that was set up to examine the long-term impact of stroke. Available for analysis were data from 604 persons with stroke, average age 69 years, and 488 controls, average age 62 years. MEASUREMENT: The SF-12 provides two summary scores, one for physical health and one for mental health. Domains of the ICF are coded to three digits, before the decimal; specific categorizations of impairments, activity limitations, and participation restrictions are coded to four digits before the decimal. RESULTS: Persons with stroke scored, on average, approximately 10 points lower than controls on physical and mental health. The ICF coding indicated that this was attributed, not surprisingly, to greater difficulty in doing moderate activities including housework, climbing stairs, and working and was not attributed to differences in pain. Differences in mental health were attributed most strongly to greater fatigue (impairment in energy), but all areas of mental health were affected to some degree. CONCLUSION: The ICF coding provided enhanced functional status information in a format compatible with the structure of administrative health databases.

Concept-match medical data scrubbing. How pathology text can be used in research.

CONTEXT: In the normal course of activity, pathologists create and archive immense data sets of scientifically valuable information. Researchers need pathology-based data sets, annotated with clinical information and linked to archived tissues, to discover and validate new diagnostic tests and therapies. Pathology records can be used for research purposes (without obtaining informed patient consent for each use of each record), provided the data are rendered harmless. Large data sets can be made harmless through 3 computational steps: (1) deidentification, the removal or modification of data fields that can be used to identify a patient (name, social security number, etc); (2) rendering the data ambiguous, ensuring that every data record in a public data set has a nonunique set of characterizing data; and (3) data scrubbing, the removal or transformation of words in free text that can be used to identify persons or that contain information that is incriminating or otherwise private. This article addresses the problem of data scrubbing. OBJECTIVE: To design and implement a general algorithm that scrubs pathology free text, removing all identifying or private information. METHODS: The Concept-Match algorithm steps through confidential text. When a medical term matching a standard nomenclature term is encountered, the term is replaced by a nomenclature code and a synonym for the original term. When a high-frequency "stop" word, such as a, an, the, or for, is encountered, it is left in place. When any other word is encountered, it is blocked and replaced by asterisks. This produces a scrubbed text. An open-source implementation of the algorithm is freely available. RESULTS: The Concept-Match scrub method transformed pathology free text into scrubbed output that preserved the sense of the original sentences, while it blocked terms that did not match terms found in the Unified Medical Language System (UMLS). The scrubbed product is safe, in the restricted sense that the output retains only standard medical terms. The software implementation scrubbed more than half a million surgical pathology report phrases in less than an hour. CONCLUSIONS: Computerized scrubbing can render the textual portion of a pathology report harmless for research purposes. Scrubbing and deidentification methods allow pathologists to create and use large pathology databases to conduct medical research.